Mentions-Modules on Online Social Networks

ABSTRACT

In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; extracting, for each of the communications, n-grams from the text of the communication; identifying mention-terms from the extracted n-grams, each mention-term being a noun-phrase; calculating a term-score for each mention-term based on a frequency of occurrence of the mention-term in the communications; and generating a mentions-module including mentions, each mention including a mention-term having a term-score greater than a threshold term-score and text from communications comprising the mention-term.

TECHNICAL FIELD

This disclosure generally relates to online social networks, and inparticular, to search queries and generating search-results on an onlinesocial network.

BACKGROUND

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. wall posts,photo-sharing, event organization, messaging, games, or advertisements)to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

Social-graph analysis views social relationships in terms of networktheory consisting of nodes and edges. Nodes represent the individualactors within the networks, and edges represent the relationshipsbetween the actors. The resulting graph-based structures are often verycomplex. There can be many types of nodes and many types of edges forconnecting nodes. In its simplest form, a social graph is a map of allof the relevant edges between all the nodes being studied.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the social-networking system may receive arequest from a client system (e.g., of a user of the online socialnetwork) associated with a particular content item. A content item maybe defined to include a link to an article, a link to a media item, anembedded content object containing a media item, or any other suitablereference to particular content. The social-networking system mayidentify communications associated with the particular content item. Theidentified communications may be authored by users of the online socialnetwork, or by any other entity. The communications may include posts,reshares, comments, messages, or other suitable communications. Thesocial-networking system 160 may generate search-results modulesrelating to the particular content item. Each of the search-resultsmodules may be of a particular module type. A search-results module mayinclude information from a subset of the identified communicationsassociated with the particular content item. The information included inthe search-results module may correspond to the particular module typeof the search-results module. As an example and not by way oflimitation, a search-results module that may be of a sentiments-moduletype may include information about the sentiments associated with asubset of the identified communications. The social-networking systemmay generate a particular search-results module if there exists a numberof communications in the subset of identified communications associatedwith the particular content item greater than a module-specificthreshold number of communications. The social-networking system maysend to the client system a customized search-results interface for theparticular content item that includes one or more of the generatedsearch-results modules. A user of the client system may use thesesearch-results interfaces (so-called “pulse pages”) and modules togauge—or “get a pulse on”—how other users feel about a content item.Across the online social network, users frequently author communications(e.g., posts, comments) that link to, or otherwise relate to, a specificcontent item. The number of these communications may be extensive,particularly with respect to content items that may be trending (e.g.,news stories, certain movie trailers). The social-networking system mayaggregate information from these communications into search-resultsmodules that may each be configured to present specific types ofinformation from these communications. As an example and not by way oflimitation, a particular search-results module may be configured topresent words (e.g., quotations, noun-phrases) most commonly used incommunications relating to a particular video, while a differentsearch-results module may be configured to present sentiments (e.g.,“Angry,” “Shocked”) associated with communications relating to the samevideo. A search-results interface may present one or more search-resultsmodules that may allow the user to quickly gauge what other users thinkabout a particular content item. As an example and not by way oflimitation, a user may access a search-results interface associated withan article about the announcement of a presidential candidate to gaugehow other users feel about the candidate or the article itself. In thisexample, upon reviewing the search-results interface, the user may beable to infer that a large group of users of the online social networksupport the candidate. For example, the user may see a sentiments-module(as described in further detail below) displaying asentiment-representation of the “Excited” sentiment (e.g., resultingfrom a relative majority of user communications using language thatindicates excitement) and a mentions-module displaying the words “soinspirational” (e.g., resulting from a relative majority of usercommunications containing those words). These modules, and others, willbe described in further detail below. The search-results interface for aparticular content item may allow the user to view a variety of relevantinformation about the particular content item and user reactions to theparticular content item in an accessible manner.

In particular embodiments, the social-networking system may generate asentiments-module for a search-results interface. The social-networkingsystem may access a plurality of communications authored by one or moreusers of the online social network. Each of the plurality ofcommunications may be associated with a particular content item and mayinclude some form of commentary (e.g., the text of the communication).Although this disclosure focuses on textual commentary, it contemplatesother forms of commentary, including media items (e.g., emojis,stickers, image files, audio files, video files), which thesocial-networking system may translate into text. As an example and notby way of limitation, in translating an audio file, thesocial-networking system 160 may use speech-to-text software torecognize any speech within the files as text. For each of the pluralityof communications associated with the particular content item, thesocial-networking system may calculate one or more sentiment-scores.Each of the sentiment-scores for the communication may correspond to aparticular sentiment and may be based on a degree to which one or moren-grams of the text of the communication match one or moresentiment-words. Sentiment-words may be n-grams that are determined tobe associated with particular sentiments. As an example and not by wayof limitation, the n-gram “great” may be a sentiment-word associatedwith the sentiments “Excited” and “Happy.” For each of the plurality ofcommunications, the social-networking system may determine an overallsentiment for the communication based on the calculated sentiment-scoresfor the communication. As an example and not by way of limitation, acommunication with a high sentiment-score for the sentiment “Happy” mayreceive an overall sentiment of “Happy.” The social-networking systemmay calculate one or more sentiment levels for the particular contentitem. Each of the sentiment levels may correspond to a sentiment, andeach sentiment level may be based on a total number of communicationsdetermined to have the overall sentiment of the sentiment level. Thesocial-networking system may generate a sentiments-module that includesone or more sentiment-representations corresponding to one or moreoverall sentiments. Each sentiment-representation may visually depict arespective sentiment. As an example and not by way of limitation, asentiment-representation corresponding to the sentiment “Happy” may be avisual depiction of a “smiley face.” The sentiments that are representedmay be sentiments having sentiment levels greater than a thresholdsentiment level. The sentiments-module may allow a user who isinterested in the particular content item to quickly understand theemotions or feelings other users are expressing in their communicationsabout the particular content item. This may even help the userunderstand the particular content item better by framing it in theappropriate emotional context. Although this disclosure focuses ondisplaying sentiment-representations within a module, it contemplatesother applications for the determined sentiments. As an example and notby way of limitation, the determined sentiments may be associated withcontent items such that the content items may be searched for based onthe associated sentiments (e.g., via a search interface, or via thesentiments-module).

In particular embodiments, the social-networking system may generate aquotations-module for a search-results interface. The social-networkingsystem may access a plurality of communications associated with aparticular content item. For each of the plurality of communications,the social-networking system may extract, from the text of thecommunication, one or more quotations from the particular content item.A quotation may be identified, for example, by quotation-indicators suchas quotation marks, or by comparing the text of the communication withtext from the particular content item. The social-networking system maydetermine, for each extracted quotation, one or more partitions of thequotation. As an example and not by way of limitation, a quotation maybe partitioned into individual sentences or clauses. Thesocial-networking system may group the extracted quotations into one ormore clusters based on a respective degree of similarity among theirrespective one or more partitions. As an example and not by way oflimitation, the social-networking system may calculate a degree ofsimilarity based on a Levenshtein distance between two quotations andmay group the two quotations together if they are within a thresholddegree of similarity. The social-networking system may calculate acluster-score for each cluster based on a frequency of occurrence of oneor more partitions of one or more quotations in the cluster incommunications associated with the particular content item. Thesocial-networking system may generate a quotations-module comprising oneor more representative quotations, each representative quotation being aquotation from a cluster having a cluster-score greater than a thresholdcluster-score. The quotations-module may allow a user who is interestedin the particular content item to quickly view popular quotations fromthe particular content item. This may help the user understand theparticular content item better (e.g., by focusing the user's attentionon particularly important or relevant portions of the particular contentitem) or may help the user understand how others view the particularcontent item (e.g., by displaying what other users felt were importantenough to quote in a communication). Although this disclosure focuses ondisplaying quotations within a module, it contemplates otherapplications for the extracted quotations. As an example and not by wayof limitation, quotations may be displayed as search results outside ofany particular module.

In particular embodiments, the social-networking system may generate amentions-module for a search-results interface. The social-networkingsystem may access a plurality of communications associated with aparticular content item. The social-networking system may extract, foreach of the plurality of communications, one or more n-grams from thetext of the communication. The social-networking system may identify oneor more mention-terms from the one or more extracted n-grams. Eachmention-term may be defined to include a noun-phrase. To ensure a morediverse content, the social-networking system may confirm that thenoun-phrase is not a quotation from the particular content item (e.g.,by comparing it against the text of quotations in the quotations-moduleor against the text of the particular content item itself). Thesocial-networking system may calculate a term-score for eachmention-term. The term-score may be based on a frequency of occurrenceof the mention-term in the plurality of communications. As an exampleand not by way of limitation, a mention-term that was included in fiftyof the plurality of communications associated with the particularcontent item may receive a greater term-score than a mention-term thatwas included in forty of the same set of communications. Thesocial-networking system may generate a mentions-module including one ormore mentions. Each of the mentions in the mentions-module may include amention-term having a term-score greater than a threshold term-score.Each of the mentions may also have text from one or more communicationsthat include the mention-term. As an example and not by way oflimitation, a mention may include at least a portion of the text of acommunication that includes the mention-term, including text thatimmediately precedes or follows the mention-term. The mention-term maybe in bold, highlighted, or otherwise distinguished from the rest of theincluded text. In the aggregate, people react to content items insimilar ways such that particular n-grams may emerge as commonly used incommunications relating to the content items. These commonly usedn-grams can be used to summarize the content items, and when the n-gramsare provided in context with users' communications, can be used toshowcase user reactions in a useful manner. For example, viewing thementions-module may allow a user who is interested in a particularcontent item to better understand the particular content item (e.g., bydisplaying words that others may have used to describe the content item)or to understand how others view the particular content item (e.g., bydisplaying words that may have been used by others to describe theirreaction to the particular content item). Although this disclosurefocuses on displaying mentions and mention-terms within a module, itcontemplates other applications for the determined mentions andmention-terms. As an example and not by way of limitation, mentions maybe displayed as search results outside of any particular module (e.g., auser may search “barack obama” and get a list of mention-terms such as“state of the union” and “signs ObamaCare”).

The embodiments disclosed above are only examples, and the scope of thisdisclosure is not limited to them. Particular embodiments may includeall, some, or none of the components, elements, features, functions,operations, or steps of the embodiments disclosed above. Embodimentsaccording to the invention are in particular disclosed in the attachedclaims directed to a method, a storage medium, a system and a computerprogram product, wherein any feature mentioned in one claim category,e.g. method, can be claimed in another claim category, e.g. system, aswell. The dependencies or references back in the attached claims arechosen for formal reasons only. However any subject matter resultingfrom a deliberate reference back to any previous claims (in particularmultiple dependencies) can be claimed as well, so that any combinationof claims and the features thereof are disclosed and can be claimedregardless of the dependencies chosen in the attached claims. Thesubject-matter which can be claimed comprises not only the combinationsof features as set out in the attached claims but also any othercombination of features in the claims, wherein each feature mentioned inthe claims can be combined with any other feature or combination ofother features in the claims. Furthermore, any of the embodiments andfeatures described or depicted herein can be claimed in a separate claimand/or in any combination with any embodiment or feature described ordepicted herein or with any of the features of the attached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with asocial-networking system.

FIG. 2 illustrates an example social graph.

FIG. 3 illustrates an example of a search-results interface with examplesearch-results modules.

FIG. 4 illustrates an example of a request including a search query thatwas submitted to the social-networking system.

FIGS. 5A and 5B illustrate two example interactive elements displayed onan interface of the online social network.

FIG. 6 illustrates an example interactive element displayed as a pushnotification.

FIG. 7 illustrates an example popular-links module.

FIG. 8 illustrates an example method for generating one or moresearch-results modules as parts of a search-results interface.

FIG. 9 illustrates an example method for generating a sentiments-module.

FIG. 10 illustrates an example method for generating aquotations-module.

FIG. 11 illustrates an example method for generating a mentions-module.

FIG. 12 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with asocial-networking system. Network environment 100 includes a clientsystem 130, a social-networking system 160, and a third-party system 170connected to each other by a network 110. Although FIG. 1 illustrates aparticular arrangement of a client system 130, a social-networkingsystem 160, a third-party system 170, and a network 110, this disclosurecontemplates any suitable arrangement of a client system 130, asocial-networking system 160, a third-party system 170, and a network110. As an example and not by way of limitation, two or more of a clientsystem 130, a social-networking system 160, and a third-party system 170may be connected to each other directly, bypassing a network 110. Asanother example, two or more of a client system 130, a social-networkingsystem 160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130,social-networking systems 160, third-party systems 170, and networks110, this disclosure contemplates any suitable number of client systems130, social-networking systems 160, third-party systems 170, andnetworks 110. As an example and not by way of limitation, networkenvironment 100 may include multiple client systems 130,social-networking systems 160, third-party systems 170, and networks110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, a social-networking system160, and a third-party system 170 to a communication network 110 or toeach other. This disclosure contemplates any suitable links 150. Inparticular embodiments, one or more links 150 include one or morewireline (such as for example Digital Subscriber Line (DSL) or Data OverCable Service Interface Specification (DOCSIS)), wireless (such as forexample Wi-Fi or Worldwide Interoperability for Microwave Access(WiMAX)), or optical (such as for example Synchronous Optical Network(SONET) or Synchronous Digital Hierarchy (SDH)) links. In particularembodiments, one or more links 150 each include an ad hoc network, anintranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, aportion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, other suitable electronicdevice, or any suitable combination thereof. This disclosurecontemplates any suitable client systems 130. A client system 130 mayenable a network user at a client system 130 to access a network 110. Aclient system 130 may enable its user to communicate with other users atother client systems 130.

In particular embodiments, a client system 130 may include a web browser132, such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLAFIREFOX, and may have one or more add-ons, plug-ins, or otherextensions, such as TOOLBAR or YAHOO TOOLBAR. A user at a client system130 may enter a Uniform Resource Locator (URL) or other addressdirecting a web browser 132 to a particular server (such as server 162,or a server associated with a third-party system 170), and the webbrowser 132 may generate a Hyper Text Transfer Protocol (HTTP) requestand communicate the HTTP request to server. The server may accept theHTTP request and communicate to a client system 130 one or more HyperText Markup Language (HTML) files responsive to the HTTP request. Theclient system 130 may render a web interface (e.g. a webpage) based onthe HTML files from the server for presentation to the user. Thisdisclosure contemplates any suitable source files. As an example and notby way of limitation, a web interface may be rendered from HTML files,Extensible Hyper Text Markup Language (XHTML) files, or ExtensibleMarkup Language (XML) files, according to particular needs. Suchinterfaces may also execute scripts such as, for example and withoutlimitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT,combinations of markup language and scripts such as AJAX (AsynchronousJAVASCRIPT and XML), and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user-profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, or a third-party system 170 to manage, retrieve, modify,add, or delete, the information stored in data store 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow athird-party system 170 to access information from the social-networkingsystem 160 by calling one or more APIs. An action logger may be used toreceive communications from a web server about a user's actions on oroff the social-networking system 160. In conjunction with the actionlog, a third-party-content-object log may be maintained of userexposures to third-party-content objects. A notification controller mayprovide information regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from a client system 130 responsive to arequest received from a client system 130. Authorization servers may beused to enforce one or more privacy settings of the users of thesocial-networking system 160. A privacy setting of a user determines howparticular information associated with a user can be shared. Theauthorization server may allow users to opt in to or opt out of havingtheir actions logged by the social-networking system 160 or shared withother systems (e.g., a third-party system 170), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 170. Location stores may be used for storinglocation information received from client systems 130 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Social Graphs

FIG. 2 illustrates an example social graph 200. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 200 in one or more data stores. In particular embodiments,the social graph 200 may include multiple nodes—which may includemultiple user nodes 202 or multiple concept nodes 204—and multiple edges206 connecting the nodes. The example social graph 200 illustrated inFIG. 2 is shown, for didactic purposes, in a two-dimensional visual maprepresentation. In particular embodiments, a social-networking system160, a client system 130, or a third-party system 170 may access thesocial graph 200 and related social-graph information for suitableapplications. The nodes and edges of the social graph 200 may be storedas data objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of the social graph 200.

In particular embodiments, a user node 202 may correspond to a user ofthe social-networking system 160. As an example and not by way oflimitation, a user may be an individual (human user), an entity (e.g.,an enterprise, business, or third-party application), or a group (e.g.,of individuals or entities) that interacts or communicates with or overthe social-networking system 160. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 202 corresponding tothe user, and store the user node 202 in one or more data stores. Usersand user nodes 202 described herein may, where appropriate, refer toregistered users and user nodes 202 associated with registered users. Inaddition or as an alternative, users and user nodes 202 described hereinmay, where appropriate, refer to users that have not registered with thesocial-networking system 160. In particular embodiments, a user node 202may be associated with information provided by a user or informationgathered by various systems, including the social-networking system 160.As an example and not by way of limitation, a user may provide his orher name, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 202 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 202 may correspond to one or more webinterfaces.

In particular embodiments, a concept node 204 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node204 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160. As an example and not by way oflimitation, information of a concept may include a name or a title; oneor more images (e.g., an image of the cover page of a book); a location(e.g., an address or a geographical location); a website (which may beassociated with a URL); contact information (e.g., a phone number or anemail address); other suitable concept information; or any suitablecombination of such information. In particular embodiments, a conceptnode 204 may be associated with one or more data objects correspondingto information associated with concept node 204. In particularembodiments, a concept node 204 may correspond to one or more webinterfaces.

In particular embodiments, a node in the social graph 200 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160. Profile interfaces may also behosted on third-party websites associated with a third-party server 170.As an example and not by way of limitation, a profile interfacecorresponding to a particular external web interface may be theparticular external web interface and the profile interface maycorrespond to a particular concept node 204. Profile interfaces may beviewable by all or a selected subset of other users. As an example andnot by way of limitation, a user node 202 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 204 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 204.

In particular embodiments, a concept node 204 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject (which may be implemented, for example, in JavaScript, AJAX, orPHP codes) representing an action or activity. As an example and not byway of limitation, a third-party web interface may include a selectableicon such as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 202 corresponding to the user and a conceptnode 204 corresponding to the third-party web interface or resource andstore edge 206 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 200 maybe connected to each other by one or more edges 206. An edge 206connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 206 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 206 connecting the first user's user node 202 to thesecond user's user node 202 in the social graph 200 and store edge 206as social-graph information in one or more of data stores 164. In theexample of FIG. 2, the social graph 200 includes an edge 206 indicatinga friend relation between user nodes 202 of user “A” and user “B” and anedge indicating a friend relation between user nodes 202 of user “C” anduser “B.” Although this disclosure describes or illustrates particularedges 206 with particular attributes connecting particular user nodes202, this disclosure contemplates any suitable edges 206 with anysuitable attributes connecting user nodes 202. As an example and not byway of limitation, an edge 206 may represent a friendship, familyrelationship, business or employment relationship, fan relationship(including, e.g., liking, etc.), follower relationship, visitorrelationship (including, e.g., accessing, viewing, checking-in, sharing,etc.), sub scriber relationship, superior/subordinate relationship,reciprocal relationship, non-reciprocal relationship, another suitabletype of relationship, or two or more such relationships. Moreover,although this disclosure generally describes nodes as being connected,this disclosure also describes users or concepts as being connected.Herein, references to users or concepts being connected may, whereappropriate, refer to the nodes corresponding to those users or conceptsbeing connected in the social graph 200 by one or more edges 206.

In particular embodiments, an edge 206 between a user node 202 and aconcept node 204 may represent a particular action or activity performedby a user associated with user node 202 toward a concept associated witha concept node 204. As an example and not by way of limitation, asillustrated in FIG. 2, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to a edge type or subtype. A concept-profile interfacecorresponding to a concept node 204 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“Imagine”) using a particular application (SPOTIFY, which is an onlinemusic application). In this case, the social-networking system 160 maycreate a “listened” edge 206 and a “used” edge (as illustrated in FIG.2) between user nodes 202 corresponding to the user and concept nodes204 corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 206 (asillustrated in FIG. 2) between concept nodes 204 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 206corresponds to an action performed by an external application (SPOTIFY)on an external audio file (the song “Imagine”). Although this disclosuredescribes particular edges 206 with particular attributes connectinguser nodes 202 and concept nodes 204, this disclosure contemplates anysuitable edges 206 with any suitable attributes connecting user nodes202 and concept nodes 204. Moreover, although this disclosure describesedges between a user node 202 and a concept node 204 representing asingle relationship, this disclosure contemplates edges between a usernode 202 and a concept node 204 representing one or more relationships.As an example and not by way of limitation, an edge 206 may representboth that a user likes and has used at a particular concept.Alternatively, another edge 206 may represent each type of relationship(or multiples of a single relationship) between a user node 202 and aconcept node 204 (as illustrated in FIG. 2 between user node 202 foruser “E” and concept node 204 for “SPOTIFY”).

In particular embodiments, the social-networking system 160 may createan edge 206 between a user node 202 and a concept node 204 in the socialgraph 200. As an example and not by way of limitation, a user viewing aconcept-profile interface (such as, for example, by using a web browseror a special-purpose application hosted by the user's client system 130)may indicate that he or she likes the concept represented by the conceptnode 204 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to the social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile interface. In response to the message, thesocial-networking system 160 may create an edge 206 between user node202 associated with the user and concept node 204, as illustrated by“like” edge 206 between the user and concept node 204. In particularembodiments, the social-networking system 160 may store an edge 206 inone or more data stores. In particular embodiments, an edge 206 may beautomatically formed by the social-networking system 160 in response toa particular user action. As an example and not by way of limitation, ifa first user uploads a picture, watches a movie, or listens to a song,an edge 206 may be formed between user node 202 corresponding to thefirst user and concept nodes 204 corresponding to those concepts.Although this disclosure describes forming particular edges 206 inparticular manners, this disclosure contemplates forming any suitableedges 206 in any suitable manner.

Search Queries on Online Social Networks

In particular embodiments, a user may submit a query to thesocial-networking system 160 by, for example, selecting a query input orinputting text into query field. A user of an online social network maysearch for information relating to a specific subject matter (e.g.,users, concepts, external content or resource) by providing a shortphrase describing the subject matter, often referred to as a “searchquery,” to a search engine. The query may be an unstructured text queryand may comprise one or more text strings (which may include one or moren-grams). In general, a user may input any character string into a queryfield to search for content on the social-networking system 160 thatmatches the text query. The social-networking system 160 may then searcha data store 164 (or, in particular, a social-graph database) toidentify content matching the query. The search engine may conduct asearch based on the query phrase using various search algorithms andgenerate search results that identify resources or content (e.g.,user-profile interfaces, content-profile interfaces, or externalresources) that are most likely to be related to the search query. Toconduct a search, a user may input or send a search query to the searchengine. In response, the search engine may identify one or moreresources that are likely to be related to the search query, each ofwhich may individually be referred to as a “search result,” orcollectively be referred to as the “search results” corresponding to thesearch query. The identified content may include, for example,social-graph elements (i.e., user nodes 202, concept nodes 204, edges206), profile interfaces, external web interfaces, or any combinationthereof. The social-networking system 160 may then generate asearch-results interface with search results corresponding to theidentified content and send the search-results interface to the user.The search results may be presented to the user, often in the form of alist of links on the search-results interface, each link beingassociated with a different interface that contains some of theidentified resources or content. In particular embodiments, each link inthe search results may be in the form of a Uniform Resource Locator(URL) that specifies where the corresponding interface is located andthe mechanism for retrieving it. The social-networking system 160 maythen send the search-results interface to the web browser 132 on theuser's client system 130. The user may then click on the URL links orotherwise select the content from the search-results interface to accessthe content from the social-networking system 160 or from an externalsystem (such as, for example, a third-party system 170), as appropriate.The resources may be ranked and presented to the user according to theirrelative degrees of relevance to the search query. The search resultsmay also be ranked and presented to the user according to their relativedegree of relevance to the user. In other words, the search results maybe personalized for the querying user based on, for example,social-graph information, user information, search or browsing historyof the user, or other suitable information related to the user. Inparticular embodiments, ranking of the resources may be determined by aranking algorithm implemented by the search engine. As an example andnot by way of limitation, resources that are more relevant to the searchquery or to the user may be ranked higher than the resources that areless relevant to the search query or the user. In particularembodiments, the search engine may limit its search to resources andcontent on the online social network. However, in particularembodiments, the search engine may also search for resources or contentson other sources, such as a third-party system 170, the internet orWorld Wide Web, or other suitable sources. Although this disclosuredescribes querying the social-networking system 160 in a particularmanner, this disclosure contemplates querying the social-networkingsystem 160 in any suitable manner.

Typeahead Processes and Queries

In particular embodiments, one or more client-side and/or backend(server-side) processes may implement and utilize a “typeahead” featurethat may automatically attempt to match social-graph elements (e.g.,user nodes 202, concept nodes 204, or edges 206) to informationcurrently being entered by a user in an input form rendered inconjunction with a requested interface (such as, for example, auser-profile interface, a concept-profile interface, a search-resultsinterface, a user interface/view state of a native applicationassociated with the online social network, or another suitable interfaceof the online social network), which may be hosted by or accessible inthe social-networking system 160. In particular embodiments, as a useris entering text to make a declaration, the typeahead feature mayattempt to match the string of textual characters being entered in thedeclaration to strings of characters (e.g., names, descriptions)corresponding to users, concepts, or edges and their correspondingelements in the social graph 200. In particular embodiments, when amatch is found, the typeahead feature may automatically populate theform with a reference to the social-graph element (such as, for example,the node name/type, node ID, edge name/type, edge ID, or anothersuitable reference or identifier) of the existing social-graph element.In particular embodiments, as the user enters characters into a formbox, the typeahead process may read the string of entered textualcharacters. As each keystroke is made, the frontend-typeahead processmay send the entered character string as a request (or call) to thebackend-typeahead process executing within the social-networking system160. In particular embodiments, the typeahead process may use one ormore matching algorithms to attempt to identify matching social-graphelements. In particular embodiments, when a match or matches are found,the typeahead process may send a response to the user's client system130 that may include, for example, the names (name strings) ordescriptions of the matching social-graph elements as well as,potentially, other metadata associated with the matching social-graphelements. As an example and not by way of limitation, if a user entersthe characters “pok” into a query field, the typeahead process maydisplay a drop-down menu that displays names of matching existingprofile interfaces and respective user nodes 202 or concept nodes 204,such as a profile interface named or devoted to “poker” or “pokemon,”which the user can then click on or otherwise select thereby confirmingthe desire to declare the matched user or concept name corresponding tothe selected node.

More information on typeahead processes may be found in U.S. patentapplication Ser. No. 12/763,162, filed 19 Apr. 2010, and U.S. patentapplication Ser. No. 13/556,072, filed 23 Jul. 2012, which areincorporated by reference.

In particular embodiments, the typeahead processes described herein maybe applied to search queries entered by a user. As an example and not byway of limitation, as a user enters text characters into a query field,a typeahead process may attempt to identify one or more user nodes 202,concept nodes 204, or edges 206 that match the string of charactersentered into the query field as the user is entering the characters. Asthe typeahead process receives requests or calls including a string orn-gram from the text query, the typeahead process may perform or causeto be performed a search to identify existing social-graph elements(i.e., user nodes 202, concept nodes 204, edges 206) having respectivenames, types, categories, or other identifiers matching the enteredtext. The typeahead process may use one or more matching algorithms toattempt to identify matching nodes or edges. When a match or matches arefound, the typeahead process may send a response to the user's clientsystem 130 that may include, for example, the names (name strings) ofthe matching nodes as well as, potentially, other metadata associatedwith the matching nodes. The typeahead process may then display adrop-down menu that displays names of matching existing profileinterfaces and respective user nodes 202 or concept nodes 204, anddisplays names of matching edges 206 that may connect to the matchinguser nodes 202 or concept nodes 204, which the user can then click on orotherwise select thereby confirming the desire to search for the matcheduser or concept name corresponding to the selected node, or to searchfor users or concepts connected to the matched users or concepts by thematching edges. Alternatively, the typeahead process may simplyauto-populate the form with the name or other identifier of thetop-ranked match rather than display a drop-down menu. The user may thenconfirm the auto-populated declaration simply by keying “enter” on akeyboard or by clicking on the auto-populated declaration. Upon userconfirmation of the matching nodes and edges, the typeahead process maysend a request that informs the social-networking system 160 of theuser's confirmation of a query containing the matching social-graphelements. In response to the request sent, the social-networking system160 may automatically (or alternately based on an instruction in therequest) call or otherwise search a social-graph database for thematching social-graph elements, or for social-graph elements connectedto the matching social-graph elements as appropriate. Although thisdisclosure describes applying the typeahead processes to search queriesin a particular manner, this disclosure contemplates applying thetypeahead processes to search queries in any suitable manner.

In connection with search queries and search results, particularembodiments may utilize one or more systems, components, elements,functions, methods, operations, or steps disclosed in U.S. patentapplication Ser. No. 11/503,093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977,027, filed 22 Dec. 2010, and U.S. patentapplication Ser. No. 12/978,265, filed 23 Dec. 2010, which areincorporated by reference.

Structured Search Queries

In particular embodiments, in response to a text query received from afirst user (i.e., the querying user), the social-networking system 160may parse the text query and identify portions of the text query thatcorrespond to particular social-graph elements. However, in some cases aquery may include one or more terms that are ambiguous, where anambiguous term is a term that may possibly correspond to multiplesocial-graph elements. To parse the ambiguous term, thesocial-networking system 160 may access a social graph 200 and thenparse the text query to identify the social-graph elements thatcorresponded to ambiguous n-grams from the text query. Thesocial-networking system 160 may then generate a set of structuredqueries, where each structured query corresponds to one of the possiblematching social-graph elements. These structured queries may be based onstrings generated by a grammar model, such that they are rendered in anatural-language syntax with references to the relevant social-graphelements. As an example and not by way of limitation, in response to thetext query, “show me friends of my girlfriend,” the social-networkingsystem 160 may generate a structured query “Friends of Stephanie,” where“Friends” and “Stephanie” in the structured query are referencescorresponding to particular social-graph elements. The reference to“Stephanie” would correspond to a particular user node 202 (where thesocial-networking system 160 has parsed the n-gram “my girlfriend” tocorrespond with a user node 202 for the user “Stephanie”), while thereference to “Friends” would correspond to friend-type edges 206connecting that user node 202 to other user nodes 202 (i.e., edges 206connecting to “Stephanie's” first-degree friends). When executing thisstructured query, the social-networking system 160 may identify one ormore user nodes 202 connected by friend-type edges 206 to the user node202 corresponding to “Stephanie.” As another example and not by way oflimitation, in response to the text query, “friends who work atfacebook,” the social-networking system 160 may generate a structuredquery “My friends who work at Facebook,” where “my friends,” “work at,”and “Facebook” in the structured query are references corresponding toparticular social-graph elements as described previously (i.e., afriend-type edge 206, a work-at-type edge 206, and concept node 204corresponding to the company “Facebook”). By providing suggestedstructured queries in response to a user's text query, thesocial-networking system 160 may provide a powerful way for users of theonline social network to search for elements represented in the socialgraph 200 based on their social-graph attributes and their relation tovarious social-graph elements. Structured queries may allow a queryinguser to search for content that is connected to particular users orconcepts in the social graph 200 by particular edge-types. Thestructured queries may be sent to the first user and displayed in adrop-down menu (via, for example, a client-side typeahead process),where the first user can then select an appropriate query to search forthe desired content. Some of the advantages of using the structuredqueries described herein include finding users of the online socialnetwork based upon limited information, bringing together virtualindexes of content from the online social network based on the relationof that content to various social-graph elements, or finding contentrelated to you and/or your friends. Although this disclosure describesgenerating particular structured queries in a particular manner, thisdisclosure contemplates generating any suitable structured queries inany suitable manner.

More information on element detection and parsing queries may be foundin U.S. patent application Ser. No. 13/556,072, filed 23 Jul. 2012, U.S.patent application Ser. No. 13/731,866, filed 31 Dec. 2012, and U.S.patent application Ser. No. 13/732,101, filed 31 Dec. 2012, each ofwhich is incorporated by reference. More information on structuredsearch queries and grammar models may be found in U.S. patentapplication Ser. No. 13/556,072, filed 23 Jul. 2012, U.S. patentapplication Ser. No. 13/674,695, filed 12 Nov. 2012, and U.S. patentapplication Ser. No. 13/731,866, filed 31 Dec. 2012, each of which isincorporated by reference.

Generating Keywords and Keyword Queries

In particular embodiments, the social-networking system 160 may providecustomized keyword completion suggestions to a querying user as the useris inputting a text string into a query field. Keyword completionsuggestions may be provided to the user in a non-structured format. Inorder to generate a keyword completion suggestion, the social-networkingsystem 160 may access multiple sources within the social-networkingsystem 160 to generate keyword completion suggestions, score the keywordcompletion suggestions from the multiple sources, and then return thekeyword completion suggestions to the user. As an example and not by wayof limitation, if a user types the query “friends stan,” then thesocial-networking system 160 may suggest, for example, “friendsstanford,” “friends stanford university,” “friends stanley,” “friendsstanley cooper,” “friends stanley kubrick,” “friends stanley cup,” and“friends stanlonski.” In this example, the social-networking system 160is suggesting the keywords which are modifications of the ambiguousn-gram “stan,” where the suggestions may be generated from a variety ofkeyword generators. The social-networking system 160 may have selectedthe keyword completion suggestions because the user is connected in someway to the suggestions. As an example and not by way of limitation, thequerying user may be connected within the social graph 200 to theconcept node 204 corresponding to Stanford University, for example bylike- or attended-type edges 206. The querying user may also have afriend named Stanley Cooper. Although this disclosure describesgenerating keyword completion suggestions in a particular manner, thisdisclosure contemplates generating keyword completion suggestions in anysuitable manner.

More information on keyword queries may be found in U.S. patentapplication Ser. No. 14/244,748, filed 3 Apr. 2014, U.S. patentapplication Ser. No. 14/470,607, filed 27 Aug. 2014, and U.S. patentapplication Ser. No. 14/561,418, filed 5 Dec. 2014, each of which isincorporated by reference.

Search-Results Interfaces for Content-Item-Specific Modules

In particular embodiments, the social-networking system 160 may receivea request from a client system 130 (e.g., of a user of the online socialnetwork) associated with a particular content item. A content item maybe defined to include a link to an article, a link to a media item, anembedded content object containing a media item, or any other suitablereference to particular content. The social-networking system 160 mayidentify communications associated with the particular content item. Theidentified communications may be authored by users of the online socialnetwork, or by any other entity (e.g., a third-party entity). Thecommunications may include posts, reshares, comments, messages, or othersuitable communications. The social-networking system 160 may generatesearch-results modules relating to the particular content item. Each ofthe search-results modules may be of a particular module type. Asearch-results module may include information from a subset of theidentified communications associated with the particular content item.The information included in the search-results module may correspond tothe particular module type of the search-results module. As an exampleand not by way of limitation, a search-results module that may be of asentiments-module type may include information about the sentimentsassociated with a subset of the identified communications. Thesocial-networking system 160 may generate a particular search-resultsmodule if there exists a number of communications in the subset ofidentified communications associated with the particular content itemgreater than a module-specific threshold number of communications. Thesocial-networking system 160 may send to the client system 130 acustomized search-results interface for the particular content item thatincludes one or more of the generated search-results modules. A user ofthe client system 130 may use these search-results interfaces (so-called“pulse pages”) to gauge—or “get a pulse on”—how other users feel about acontent item. Across the online social network, users frequently authorcommunications (e.g., posts, comments) that link to, or otherwise relateto, a specific content item. The number of these communications may beextensive, particularly with respect to content items that may betrending (e.g., news stories, certain movie trailers). Thesocial-networking system 160 may aggregate information from thesecommunications into search-results modules that may be each configuredto present specific types of information. As an example and not by wayof limitation, a particular search-results module may be configured topresent words (e.g., quotations, noun-phrases) that may be most commonlyused in communications relating to a particular video, while a differentsearch-results module may be configured to present sentiments (e.g.,emotions or feelings, such as “Angry” or “Shocked”) associated withcommunications relating to the same video. A search-results interfacemay present one or more search-results modules that may allow the userto quickly gauge what other users think about a particular content item.As an example and not by way of limitation, a user may access asearch-results interface associated with an article about theannouncement of a presidential candidate to gauge how other users feelabout the candidate or the article itself. In this example, uponreviewing the search-results interface, the user may be able to inferthat a large group of users of the online social network support thecandidate. For example, the user may see a sentiments-module displayinga sentiment-representation of the “Excited” sentiment. In this example,the “Excited” sentiment may be displayed because there exists a relativemajority of user communications indicating excitement (i.e., where thenumber of user communications indicating excitement is not necessarilygreater than half of the communications, but is greater than the numberof communications with any other indicated sentiment). The user may alsosee a mentions-module displaying the words “so inspirational” (e.g.,resulting from a relative majority of user communications containingthose words). These modules, and others, will be described in furtherdetail below. The search-results interface for a particular content itemmay allow the user to view a variety of relevant information about theparticular content item and user reactions to the content item in anaccessible manner.

As used herein, the term “interface” may include a webpage, a userinterface of a native application on a computing device, or any othersuitable interface. The term “communication” as used herein may includea communication made by a user of an online social network, and mayinclude both private and public communications. Public communicationsmay include posts, reshares, comments, or any other suitable means ofcommunicating to a relatively large group of users. Privatecommunications may include emails, private messages, chats, or any othersuitable means of communicating directly to a specific set of users. Theterm “post” as used herein may include a communication authored by auser on a newsfeed interface or homepage of the online social network,on a user's own page of the online social network (e.g., the user'stimeline or wall), on the page of the user's online-social-networkconnection (e.g., a timeline or wall of the user's first-degreeconnection or “friend”), on the page of a group on the online socialnetwork (e.g., a timeline or wall of a group related to a hobby), or onanother suitable interface of the online social network, where thecommunication does not reference another communication on the onlinesocial network. As used herein, the term “reshare” may include acommunication authored by a user on the online social network, where thecommunication references another communication on the online socialnetwork (e.g., the reshare may link to or embed a post). As used herein,the term “comment” may include a communication authored by a user on theonline social network that responds to another communication on theonline social network (e.g., a post or a reshare). The term may alsoinclude a reply to a comment. As used herein, the term “private message”may include any nonpublic communications between or among one or moreusers on the online social network. As used herein, the term “contentitem” may refer to any form of content (not including communications)that may be shared on the online social network. As an example and notby way of limitation, a content item may be defined to include anarticle, a description of a product on a commercial website, a mediaitem (e.g., a video clip, an audio clip), or any other suitable content.While this disclosure focuses on describing the presentation ofsearch-results modules in a webpage or other user interface, thisdisclosure contemplates presenting the search-results modules in othercontexts (e.g., pushed, emailed, or otherwise sent to a client system130). Furthermore, while this disclosure focuses on search-results basedon communications associated with content items, this disclosurecontemplates search-results based on any other suitable communications,including communications that are not associated with any content items.Furthermore, while this disclosure focuses on describing communicationson the online social network, the disclosure contemplates communicationson any suitable platform (e.g., a text-messaging platform, an emailplatform, a Local Area Network).

FIG. 3 illustrates an example of a search-results interface with examplesearch-results modules. The example search-results interface in FIG. 3includes a content item 310 and search-results modules 320, 330, and 340associated with the content item 310, along with a related user post350. Search-results modules may refer to a grouping of objects (e.g.user profiles, posts, photos, webpages, etc.) or references to objectsidentified in response to a search query. More information onsearch-results modules may be found in U.S. patent application Ser. No.14/244,748, filed 3 Apr. 2014, and U.S. patent application Ser. No.14/470,583, filed 27 Aug. 2014, both of which are incorporated byreference. In particular embodiments, the social-networking system 160may receive a request associated with a particular content item. Therequest may be from a client system 130 of a user (e.g., a user of theonline social network). In particular embodiments, the request mayinclude a search query submitted by the client system 130 of the user.The search query may be a text string either entered by the user into aquery field or otherwise inputted in any suitable manner (e.g., byselecting among auto-suggested search queries). The search query may bea structured or unstructured query, as described above. FIG. 4illustrates an example of a request including a search query that wassubmitted to the social-networking system 160. The search query mayinclude one or more n-grams associated with a particular content item ofa search-results interface. As an example and not by way of limitation,referencing FIG. 4, the user may have entered the search query “vmas” inthe query field 410, which may be an n-gram associated with the MTVVideo Music Awards ceremony. An n-gram may be associated with aparticular content item if it is associated with a topic that theparticular content item is associated with on the online social network.As an example and not by way of limitation, the social-networking system160 may determine that an n-gram of a search query is associated withone or more topics by accessing an index that indexes topics withassociated keywords and then searching for keywords that match an n-gramof a search query. In this example, the social-networking system 160 maydetermine that a content item is associated with one or more topics inmuch the same way by extracting n-grams associated with the content item(e.g., from associated communications, metadata of the content item, anytext that is part of the content item). The social-networking system 160may determine that an n-gram of a search query is associated with acontent item if there is a sufficient match between their respectivekeywords. More information on topic association and keyword matching maybe found in U.S. patent application Ser. No. 13/167,701, filed 23 Jun.2011, and U.S. patent application Ser. No. 14/585,782, filed 30 Dec.2014, each of which is incorporated by reference. The social-networkingsystem 160 may accordingly associate the request with content itemsrelated to the Video Music Awards. In particular embodiments, therequest may include a user selection of an interactive element. Theinteractive element may be generated for the particular content item ifa search-results interface may be generated for the particular contentitem. Once generated, the interactive element may be displayed to theuser to allow the user to select it. The interactive element may bedisplayed in any suitable manner on a interface of the online socialnetwork. FIGS. 5A and 5B illustrate two example interactive elements 510and 520 displayed on a interface of the online social network. Theinteractive element may alternatively be displayed as a notification(e.g., a push notification) outside of a interface of the online socialnetwork. FIG. 6 illustrates an example interactive element 610 displayedas a notification. In sending an interactive element as a notification,an application associated with the social-networking system 160 may berunning in the client system 130 (e.g., by running as a backgroundapplication). The application may send the interactive element to theclient system 130 for display to the user. As an example and not by wayof limitation, referencing FIG. 6, the interactive element 610 may bedisplayed when a search-results interface is generated for the videoassociated with the interface currently being viewed by the user (e.g.,“Lip Sync Battle with Will Ferrell, Kevin Hart and Jimmy Fallon”, asillustrated in FIG. 6) on a third-party website or application (e.g., ona web browser application, an application associated with a third-partyvideo-sharing platform). An interactive element may be selected by theuser in any suitable manner (e.g., tapping it on a touchscreen,performing a suitable gesture on or off the screen, clicking on it usingan input device). Although this disclosure describes receivingparticular requests from a particular system in a particular manner,this disclosure contemplates receiving any suitable requests from anysuitable system in any suitable manner.

In particular embodiments, the social-networking system 160 may identifyone or more communications authored by one or more users of the onlinesocial network. Each identified communication may be associated with theparticular content item. For the purposes of this disclosure, acommunication is defined to be associated with a content item if itincludes the content item (e.g., embedded as a content object), a linkto an instance of the content item, or otherwise references the contentitem (e.g., a communication including a threshold amount of textmatching keywords associated with the content item). A communication mayalso be associated with a content item if it is a response (e.g., acomment, a responsive private message) to a communication that isassociated with a content item. As an example and not by way oflimitation, the social-networking system 160 may identify communicationsthat include a link to a particular article. The identification of acommunication may be further based on a degree of separation orsocial-graph affinity between the querying user and the author of thecommunication (or between the user and the communication itself). As anexample and not by way of limitation, the identified communications mayinclude only communications authored by first-degree connections of thequerying user. As another example and not by way of limitation, theidentified communications may include only communications authored byusers for which the querying user has a threshold level of affinity(e.g., as determined by an affinity coefficient). In particularembodiments, the communications may be identified by first accessing acommunication index. The communication index may index a plurality ofcontent items and, for each content item, one or more communicationsassociated with the content item. As an example and not by way oflimitation, referencing FIG. 3, the communication index may index thecontent item 310 with all posts associated with the content item 310.Although this disclosure describes identifying particular communicationsin a particular manner, this disclosure contemplates identifying anysuitable communications in any suitable manner.

In particular embodiments, the social-networking system 160 may generateone or more search-results modules related to the particular contentitem. As an example and not by way of limitation, referencing FIG. 3,the social-networking system 160 may have generated search-resultsmodules 320, 330, and 340, which are all related to the content item310. Each of the generated search-results modules may be of a particularmodule type and may include information from a subset of the identifiedcommunications. The information in each search-results module maycorrespond to the particular module type of the search-results module.As an example and not by way of limitation, referencing FIG. 3, thesearch-results module 320 may include information related to one or moresentiments determined to be associated with the subset of identifiedcommunications. As another example and not by way of limitation, againreferencing FIG. 3, the search-results module 330 may includeinformation related to quotations extracted from the subset ofidentified communications. In particular embodiments, thesocial-networking system 160 may generate a particular search-resultsmodule when a number of communications in the subset of identifiedcommunications for the particular search-results module is greater thana threshold number of communications. As an example and not by way oflimitation, referencing FIG. 3, the search-results module 320 may begenerated only if the number of identified communications in the subset(e.g., communications associated with the content item 310 for whichsentiments may be determined) is greater than ten thousand. In thisexample, the subset of identified communications includes at least15,233 communications (6,595 angry posts+5,366 shocked posts+3,272motivated posts), which is greater than ten thousand communications.This may prompt the social-networking system 160 to generate thesearch-results module 320. In particular embodiments, thesocial-networking system 160 may generate a particular search-resultsmodule when a number of communications in the subset of identifiedcommunications that converge on a single result in the search-resultsmodule is greater than a threshold number of communications. As anexample and not by way of limitation, again referencing FIG. 3, thesearch-results module 320 may be generated only if there exists at leastone overall sentiment (i.e., a single result) that is associated with anumber of identified communications in the subset that is greater thansix thousand. In this example, the social-networking system 160 maygenerate the search-results module 320, because at least the “Angry”sentiment is an overall sentiment that is associated with more than sixthousand communications (at least 6,595 posts). The threshold number ofcommunications may be module specific, in that each type of module mayhave its own module-specific threshold number, which may be differentdepending on the module type of a particular search-results module. Themodule-specific threshold number may correspond to the number ofcommunications in the subset of identified communications for thesearch-results module (i.e., communications from which informationrelevant to the specific module can be extracted). As an example and notby way of limitation, referencing FIG. 3, the search-results module 320may have a module-specific threshold number of one thousand, while thesearch-results module 330 may have a module-specific threshold number often. In this example, the social-networking system 160 may generate thesearch-results module 320 if there are 1,001 communications for which anoverall sentiment is determined, and may generate the search-resultsmodule 330 if there are eleven communications for which a quotation isextracted. Although this disclosure describes generating particularmodules in a particular manner, this disclosure describes generating anysuch items that are suitable in a suitable manner.

In particular embodiments, the social-networking system 160 may send acustomized search-results interface (e.g., a search-results page) forthe particular content item requested from the client system 130. Thecustomized search-results interface may comprise one or moresearch-results modules. The search-results interface may also includeother related items such as related user post 350 or links to relatedcontent items. The search-results interface may be sent to the clientsystem 130 that sent the request associated with the particular contentitem. In particular embodiments, a search-results module may begenerated even if there are not a threshold number of posts, but it mayonly be sent to the client system 130 if the threshold is exceeded. Inparticular embodiments, the search-results module may be sent to theclient system 130 even if the threshold is not exceeded if the queryinguser submits a suitable request. As an example and not by way oflimitation, the querying user who is viewing a content item's webinterface that does not display one or more of the search-resultsmodules (e.g., because the number of identified communications do notexceed the respective module-specific threshold numbers) may select aninteractive element labeled “See More” (or a suitable equivalent),prompting the social-networking system 160 to generate and/or send themissing search-results modules for the content item. Thesocial-networking system 160 may only send a missing search-resultsmodule if there is at least one communication from which informationrelevant to the module type can be extracted (e.g., for asentiments-module, at least one communication for which a sentiment canbe determined). The search-results modules on the search-resultsinterface may be arranged in any suitable manner. As an example and notby way of limitation, the search-results module may be stackedvertically or horizontally. The search-results modules may not allappear on the search-results interface at once and may require a furtheruser action to cause one or more of the modules to appear. The order inwhich the one or more modules are displayed may be determined, at leastin part, by calculating a module-score for the one or moresearch-results modules to be displayed. The module-score may represent arelevance or quality of the module, and may provide a way for thesocial-networking system to determine an optimal order in which todisplay the various search-results modules. The module-score may bebased on information in a user-preference file associated with the firstuser. The user-preference file may be stored on the client system 130, aserver of the social-networking system 160, a third-party system 170, orany combination thereof. The user-preference file may contain preferenceinformation specified by the user. As an example and not by way oflimitation, referencing FIG. 3, the user may have indicated a preferencefor search-results modules that are of the same type as thesearch-results module 320 (i.e., sentiment-modules). This informationmay be stored in the user-preference file. The module-score may be basedon a level of engagement among users of the online social network. Thelevel of engagement may be a measure of how much users engage withparticular search-results modules. The social-networking system 160 mayaward a higher module-score to a search-results module if it has arelatively high level of engagement than otherwise. Thesocial-networking system 160 may determine the level of engagement basedon the amount of time users spend on particular search-results modules(which may be determined by measuring the amount of time users spend ona portion of the screen, or on a view state in the case of a mobileclient system, that includes particular search-results modules), thenumber of interactions (e.g., expressing social signals such as “likes”or comments, selection of on interactive elements) users have withparticular search-results modules, any other suitable metric formeasuring user engagement or interest with particular search-resultsmodules, or any combination thereof. As an example and not by way oflimitation, referencing FIG. 3, the search-results module 340 may allowusers to “like” particular “mentions” (see below for a furtherdescription of mentions). In this example, the social-networking system160 may keep track of the number of users that have used thesearch-results module 340 to like one or more mentions and base itsdetermination of the module-score accordingly (e.g., awarding a highermodule-score to the search-results module 340 if there were a relativelyhigh number of users who used the search-results module to likementions). As another example, again referencing FIG. 3, thesearch-results module 320 may include one or more interactive elements,which may be in the form of a sentiment-representation such as thesentiment-representation 360. The social-networking system 160 may keeptrack of the number of users selecting the one or more interactiveelements (e.g., the sentiment-representation 360) and base itsdetermination of the module-score accordingly. As yet another exampleand not by way of limitation, referencing FIG. 3, the search-resultsmodule 320 may also include an interactive element (not shown) thatallows a user to like a particular sentiment. The social-networkingsystem 160 may keep track of the number of users like sentiments in thesearch-results module and base its determination of the module-scoreaccordingly. In particular embodiments, the level of engagement may bebased on how much users engage with a particular module type. As anexample and not by way of limitation, referencing FIG. 3, thesocial-networking system 160 may determine how much users engage withsearch-results modules that are of the same type as search-resultsmodule 320 (e.g., determining that users often activate interactiveelements within such modules). The level of engagement may be measuredover a defined period of time or may be measured instantaneously(reflecting the most current level of engagement with search-resultsmodules on the online social network). In particular embodiments, themodule-score may be based on a historical level of engagement by thequerying user with the respective particular search-results module orthe respective module type. As an example and not by way of limitation,referencing FIG. 3, the user may have often engaged in the past withsearch-results modules that are of the same type as search-resultsmodule 320. In this example, the search-results module 320 may beawarded a relatively high module-score. The user's historical level ofengagement may be stored with the user-preference file associated withthe user. In particular embodiments, the module-score may be based onthe particular content item. For certain content items, by the nature ofthe content they contain, certain types of information (reflected incertain search-results modules) may be more interesting than otherinformation (reflected in other search-results modules). As an exampleand not by way of limitation, a Supreme Court written opinion mayprovide interesting quotations that may have been picked upon by usersof the online social network. In this example, a search-results modulecontaining information related to quotations from the Supreme Courtwritten opinion may receive a relatively high module-score. Inparticular embodiments, the module-score may be based on demographicinformation (e.g., age, gender, race, nationality, location) of thequerying user. As an example and not by way of limitation, referencingFIG. 3, a fifteen-year old user may be more interested in thesearch-results module 340 (i.e., a mentions-module, which may highlightthe terms most commonly mentioned in communications) than thesearch-results module 330 (i.e., a quotations-module, which may presentthe quotations most commonly present in communications). The conversemay be true of a fifty-year old user. In the same example, thisinformation about the relative interests in the search-results modulesamong different age groups may be based on pre-set parameters or may bedetermined dynamically by the social-networking system 160 (e.g., bymeasuring relative levels of engagement among users of the differentdemographic groups). In particular embodiment, the calculatedmodule-score may be a function of any combination of the factorsdescribed above or any other suitable factor on which the module-scoremay be based. As an example and not by way of limitation, the functionfor calculating a module-score may be represented by the followingexpression: f(m₁,m₂,m₃), where m₁, m₂, and m₃ are three differentfactors. The calculated module-score may alternatively be a sum ofdifferent functions that may be weighted in a suitable manner (e.g., theweights being pre-determined by the social-networking system 160). As anexample and not by way of limitation, the function for calculating amodule-score may be represented by the following expression: Af₁(m₁,m₂)+B f₂(m₃), where m₁, m₂, and m₃ are three different factors,and where A and B are two different weights. Although this disclosuredescribes sending a particular interface in a particular manner, thisdisclosure contemplates sending any suitable interface in any suitablemanner.

In particular embodiments, the order of search-results modules may alsobe pre-determined. As an example and not by way of limitation,referencing FIG. 3, the search-results module 320, if generated andsent, may always be set to appear as the first search-results module.Alternatively, in the same example, the search-results module 320 maysimply receive an increase in its module-score, which may yet beovercome by a different search-results module that nonetheless receivesa higher module-score.

In particular embodiments, the social-networking system 160 may generatea popular-links module that may be sent to the client system 130. FIG. 7illustrates an example popular-links module. The popular-links modulemay include one or more references to one or more other content items. Areference to a content item may include a reference to a search-resultsinterface or other suitable interface for the content item. Each of theother content items referenced in the popular-links module may havegreater than a threshold number of associated communications. Thethreshold number may be a threshold score or rank, such that thepopular-links module may only include references to content items withthe highest number of associated communications. As an example and notby way of limitation, the popular-links module may include onlyreferences to content items that are determined to be currently“trending,” i.e., content items with the highest number of associatedcommunications within a specific timeframe (e.g., the past 48 hours). Asanother example and not by way of limitation, the popular-links modulemay include only references to content items that are associated withtopics that are determined to be currently trending. More information ondetermining trending items such as topics may be found in U.S. patentapplication Ser. No. 14/858,366, filed 18 Sep. 2015, which isincorporated by reference. As yet another example and not by way oflimitation, the popular-links module may include only references to thethree content items with the highest number of associatedcommunications. In particular embodiments, the popular-links module maybe sent in response to a search query (e.g., as part of a genericsearch-results interface) and may display links that are related, orotherwise relevant, to the search query. As an example and not by way oflimitation, referencing FIG. 7, the popular-links module 710 may be sentin response to a search query including the n-gram “VMAs.” In particularembodiments, a popular-links module may be sent along with thesearch-results interface that the user is currently viewing. Thepopular-links module may include links related to the content item ofthe search-results interface being viewed. As an example and not by wayof limitation, referencing FIG. 3, the social-networking system 160 maygenerate a popular-links module related to the content item 310, whichmay include a link to a video related to the content item 310 (e.g., auser's reaction video on John Oliver's comments regarding food waste), alink to a different search-results interface related to the content item310 (e.g., a search-results interface for a book about food wastegenerally), or a link to any other suitable search-results interface(e.g., a trending-topics interface). The popular-links module mayinclude links that are not related to the content item of thesearch-results interface being viewed. Such links may instead simply berelated to content items that are popular (e.g., content items for whicha threshold number of users have authored communications within aspecified period) on the online social network. Although this disclosuredescribes generating a particular popular-links module in a particularmanner, this disclosure contemplates generating any similarly suitablemodule in any suitable manner.

In particular embodiments, the information of one or more search-resultsmodules may be altered based on a user selection made by the user of theclient system 130. The user selection may include the selection of aninteractive element. The user selection may occur before or after thesearch-results modules or the associated search-results interface issent to the user. As an example and not by way of limitation,referencing FIG. 3, the sentiment-representation 360 (e.g., representingthe sentiment “Motivated”) may be an interactive element, in which casethe user may select the sentiment-representation 360. In this example,the information of the search-results modules 330 and 340 may be alteredin a manner suitably responsive to the sentiment that was selected. Forexample, the search-results modules 330 and 340 may be updated inresponse to a user selecting the “Motivated” sentiment-representation360 to show only quotations and mentions, respectively, that areassociated with the sentiment “Motivated” (i.e., quotations and mentionsthat are extracted from communications determined to have the overallsentiment “Motivated,” the determination of which is further describedbelow).

In particular embodiments, the social-networking system 160 may send, tothe client system 130 of the user, communications or content itemsresponsive to a search query corresponding to one or more particularsentiments. A search query may correspond to a particular sentiment ifit includes one or more n-grams that are synonymous with, or areotherwise closely associated with, the particular sentiment. Thesocial-networking system 160 may access a communication index that mayindex communications and, for each communication, one or more sentimentsassociated with the communication. The social-networking system 160 mayidentify one or more responsive communications from the communicationindex. The responsive communications may be communications that areassociated with one or more sentiments that match the particular one ormore sentiments corresponding to the search query. The social-networkingsystem 160 may send one or more of the responsive communications to theclient system 130 of the user. As an example and not by way oflimitation, the user of the client system 130 may enter a search queryincluding the n-gram “motivational.” In this example, the socialnetworking system 160 may return communications associated with thesentiment “Motivated.” In particular embodiments, the social-networkingsystem 160 may send content items, alternatively or in addition tocommunications, using a similar process. In such an embodiment, thesocial-networking system 160 may access an index (e.g., thecommunication index) that indexes content items with respective one ormore sentiments (e.g., as determined by their respective associatedcommunications, as determined by metadata of the content item). Thesocial-networking system 160 may then identify responsive content itemsin a manner similar to that in which it identifies responsivecommunications. As an example and not by way of limitation, thesocial-networking system 160 may return links to content items (e.g.,video files, audio files) that are associated with the sentiment“Motivated” in response to a search query including the n-gram“motivational.” In particular embodiments, the social-networking system160 may send, to the client system 130 of the user, search-resultsmodules with information from the responsive communications or contentitems. In particular embodiments, the social-networking system 160 mayupdate search-results modules that may have already been generated orsent to the user. As an example and not by way of limitation,referencing FIG. 3, a user who may be viewing the search-resultsinterface with the search-results modules 320, 330, and 340 may enter asearch query including the n-gram “motivational,” in response to whichthe social-networking system 160 may alter the contents ofsearch-results modules 330 and 340. In particular embodiments, thesocial-networking system 160 may perform any combination of the above inresponse to a search query corresponding to one or more sentiments.

One or more of the search-results modules may be updated after they aresent to reflect the latest information from the latest subset ofidentified communications. These updates may be regular (e.g., occurringperiodically at specific time intervals). Alternatively or additionally,it may be updated whenever a threshold number of update-communicationsare newly identified (e.g., as new communications are made) as beingwithin the subset of identified communications for a search-resultsmodule. This threshold number of update-communications may be modulespecific. As an example and not by way of limitation, the search-resultsmodule 320 may be updated when there are twenty communications newlyidentified as being within the subset for the search-results module 320.In the same example, the search-results module 340 may be updated whenthere are five communications newly identified as being within thesubset for the search-results module 340. In particular embodiments, asearch-results module may be updated by generating and sending areplacement search-results module containing updated information. As anexample and not by way of limitation, a replacement search-resultsmodule may be sent every five minutes with one or more other postsreplacing one or more of the displayed posts. In particular embodiments,a search-results module may be updated by sending updated information toadd to or replace at least some of the information in a search-resultsmodule. As an example and not by way of limitation, referencing FIG. 3,the search results module 320 may be updated by sending new informationabout the number of angry communications (e.g., posts, in this instance)that are associated with content item 310, replacing the number “6,595”with the number “6,596” to reflect the fact that an additional angrycommunication has been authored on the online social network.

FIG. 8 illustrates an example method 800 for generating one or moresearch-results modules as parts of a search-results interface. Themethod may begin at step 810, where the social-networking system 160 mayreceive, from a client system 130 of a first user of an online socialnetwork, a request associated with a particular content item. At step820, the social-networking system 160 may identify one or morecommunications authored by one or more users of the online socialnetwork, each identified communication being associated with theparticular content item. At step 830, the social-networking system 160may generate one or more search-results modules related to theparticular content item, each search-results module being of aparticular module type, wherein each search-results module comprisesinformation from a subset of the identified communications, theinformation corresponding to the particular module type of thesearch-results module, and wherein a number of communications in thesubset of identified communications comprising each search-resultsmodule is greater than a module-specific threshold number ofcommunications. At step 840, the social-networking system 160 may send,to the client system 130, a search-results interface comprising one ormore of the search-results modules. Particular embodiments may repeatone or more steps of the method of FIG. 8, where appropriate. Althoughthis disclosure describes and illustrates particular steps of the methodof FIG. 8 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 8 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for generating one or more search-resultsmodules as parts of a search-results interface including the particularsteps of the method of FIG. 8, this disclosure contemplates any suitablemethod for sending data corresponding to media items based on a userinput including any suitable steps, which may include all, some, or noneof the steps of the method of FIG. 8, where appropriate. Furthermore,although this disclosure describes and illustrates particularcomponents, devices, or systems carrying out particular steps of themethod of FIG. 8, this disclosure contemplates any suitable combinationof any suitable components, devices, or systems carrying out anysuitable steps of the method of FIG. 8.

Sentiments-Modules

In particular embodiments, the social-networking system may generate asentiments-module for a search-results interface. As discussedpreviously, the social-networking system 160 may access a plurality ofcommunications authored by one or more users of an online socialnetwork. Each of the plurality of communications may be associated witha particular content item and may include some form of commentary, whichmay include a text of the communication. Although this disclosurefocuses on textual commentary, it contemplates other forms ofcommentary, which may include media items (e.g., emojis, stickers, imagefiles, audio files, video files), which the social-networking system 160may translate into text. As an example and not by way of limitation, intranslating an audio file, the social-networking system 160 may usespeech-to-text software to recognize any speech within the files astext. As an example and not by way of limitation, the social-networkingsystem may translate a scanned image of a document into text usingoptical-character-recognition software. For each of the plurality ofcommunications, the social-networking system 160 may calculate one ormore sentiment-scores. Each of the sentiment-scores for thecommunication may correspond to a particular sentiment and may be basedon a degree to which one or more n-grams of the text of thecommunication match one or more sentiment-words. Sentiment-words may ben-grams that are determined to be associated with particular sentiments.As an example and not by way of limitation, the n-gram “great” may be asentiment-word associated with the sentiments “Excited” and “Happy.” Foreach of the plurality of communications, the social-networking system160 may determine an overall sentiment for the communication based onthe calculated sentiment-scores for the communication. As an example andnot by way of limitation, a communication with a high sentiment-scorefor the sentiment “Happy” may receive an overall sentiment of “Happy.”The social-networking system 160 may calculate one or more sentimentlevels for the particular content item. Each of the sentiment levels maycorrespond to a sentiment. Each sentiment level may be based on a totalnumber of communications determined to have the overall sentiment of thesentiment level. The social-networking system 160 may generate asentiments-module that includes one or more sentiment-representationscorresponding to one or more overall sentiments. Each of thesentiment-representations may visually depict a respective sentiment.The sentiments that are represented may be sentiments having sentimentlevels greater than a threshold sentiment level. The sentiments-modulemay allow a user who is interested in the particular content item toquickly understand how others feel about the particular content item.This may even help the user understand the particular content itembetter by framing it in the appropriate emotional context. Although thisdisclosure focuses on displaying sentiment-representations within amodule, it contemplates other applications for the determinedsentiments. As an example and not by way of limitation, the determinedsentiments may be associated with content items such that the contentitems may be searched for based on the associated sentiments. As anotherexample and not by way of limitation, adhering to privacy constraints(e.g., a user may opt in or opt out of having sentiment-relatedinformation associated with the user used in the online social networkfor any or all purposes), the sentiments associated with a particularcontent item (e.g., an advertisement video for a new car) andinformation related to the users (e.g., demographic information) whosecommunications expressed particular sentiments may be compiled into areport for interested parties (e.g., a company may study such a reportto understand that a particular type of car commercial or a particularcar appeals to a certain demographic more than other demographics).

In particular embodiments, the social-networking system 160 may access aplurality of communications authored by one or more users of an onlinesocial network. Each of the accessed communication may be associatedwith a particular content item. The accessed communications may includea text of the communication. The text of the communication may reflectuser commentary relating to the particular content item. In particularembodiments, an accessed communication may alternatively or additionallyinclude other forms of commentary such as media items (e.g., emojis,stickers, image files, audio files, video files, image files). In suchcases, the social-networking system 160 may translate media items intotext. Translating a media item into text may include substituting themedia item with one or more n-grams or keywords associated with themedia item. More information on translating media items incommunications may be found in U.S. patent application Ser. No.14/952,707, filed 25 Nov. 2015, which is incorporated by reference.Translating a media item into text may also include the use of softwarethat recognizes text within a media item. As an example and not by wayof limitation, in translating an audio or video file, thesocial-networking system 160 may use speech-to-text software torecognize any speech within the files as text. As another example andnot by way of limitation, in translating an image file, thesocial-networking system 160 may be able to analyze imagecharacteristics such as pixel values or gradients to determine shapesand may then recognize persons (e.g., a particular user) or concepts(e.g., the Eiffel Tower) that the image represents. Thesocial-networking system 160 may then translate the image into textdescribing the person (e.g., a name of the user, “John Smith”;descriptors of the user, “male,” “26-year-old”) or text describing theconcept (the text “eiffel tower,” “france”) being represented in theimage. More information about analyzing images to recognize persons orconcepts may be found in U.S. patent application Ser. No. 13/959,446,filed 5 Aug. 2013, and U.S. patent application Ser. No. 14/983,385,filed 29 Dec. 2015, which are incorporated by reference. As anotherexample and not by way of limitation, in translating an image file thatcontains text (e.g., a scanned image of a document), thesocial-networking system 160 may use optical-character-recognitionsoftware to recognize text within the file. In particular embodiments,an accessed communication may include both text and other forms ofcommentary. Although this disclosure describes accessing particularcommunications in a particular manner, this disclosure contemplatessending any suitable communications in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate, for each of the plurality of communications, one or moresentiment-scores corresponding to one or more sentiments, respectively.The sentiment-score for a communication may be a quantitative measure ofthe amount of a respective sentiment in the communication (e.g., on ascale of 0 to 1, with 1 representing the highest amount). In calculatinga sentiment-score for a particular communication, the social-networkingsystem 160 may extract the n-grams associated with the communication.The sentiment-score may be based on a degree to which one or more of theextracted n-grams of the text of the communication (or the n-gramsassociated with other forms of commentary in the post such as mediaitems) match one or more sentiment-words associated with the one or moresentiments. Sentiment-words may be n-grams that are determined to beassociated with particular sentiments. The social-networking system 160may determine that a particular sentiment-word is associated with one ormore sentiments through a variety of ways. In particular embodiments,the social-network may access a dictionary or index that ispre-populated with such associations (e.g., the sentiment word “wow” maybe indexed with the sentiment “Excited”). In particular embodiments, thesocial-networking system 160 may access a sentiment-word index that is,or has been, populated using information from communications on theonline social network. The sentiment-word index may indexsentiment-words with their respective sentiments. The social-networkingsystem 160 may extract n-grams from one or more communications. The oneor more communications from which the n-grams are extracted may bedetermined to be associated with particular sentiments based onsentiment expressions in the communications. As an example and not byway of limitation, a user may author a post including the n-gram “fomo”(an acronym for “Fear of Missing Out”) and express a particularsentiment through a user selection (e.g., by selecting the status“feeling anxious” among a list of statuses in a dropdown menu). In thisexample, the social-networking system 160 may associate the n-gram“fomo” with the sentiment “Anxious” (e.g., in an index), based on thefact that the status “feeling anxious” may itself be associated withthat sentiment. As another example and not by way of limitation, a usermay author the post including the n-gram “fomo” but may also express asentiment by including the hashtag “#anxious” or “#concerned.” In thisexample, the social-networking system 160 may associate the n-gram“fomo” with the sentiment “Anxious,” based on the fact that the hashtagused may itself be associated with that sentiment. As another exampleand not by way of limitation, a user may author the post including then-gram “fomo” and may express a sentiment by including the text“anxious” or “nail-biter” (i.e., an idiomatic expression that isassociated with anxiousness). In this example, the social-networkingsystem 160 may associate the n-gram “fomo” with the sentiment “Anxious,”based on the fact that the text (i.e., “anxious” or “nail-biter”) may beassociated with that sentiment (e.g., if they are already in thesentiment-word index from a past iteration of this process). In theseexamples and for the purpose of this disclosure, the n-gram “fomo” maybe a sentiment-word for the sentiment “Anxious.” The sentiment-wordindex may be updated regularly—for example, as new communications areauthored. The extraction of n-grams or text in determiningsentiment-words to populate the sentiment-word index may involve the useof a term frequency-inverse document frequency (TF-IDF) analysis of thetext of the communication (or the n-grams associated with other forms ofcommentary in this post such as media items) in relation to a set ofcommunications. The TF-IDF is a statistical measure used to evaluate howimportant a word is to a document (e.g., a communication) in acollection or corpus (e.g., a set of communications). The importanceincreases proportionally to the number of times a word appears in aparticular document, but is offset by the frequency of the word in thecorpus of documents. The importance of a word in a particular documentis based in part on the term count in a document, which is simply thenumber of times a given term (e.g., a word) appears in the document.This count may be normalized to prevent a bias towards longer documents(which may have a higher term count regardless of the actual importanceof that term in the document) and to give a measure of the importance ofthe term t within the particular document d. Thus we have the termfrequency tf(t,d), defined in the simplest case as the occurrence countof a term in a document. The inverse-document frequency (idf) is ameasure of the general importance of the term which is obtained bydividing the total number of documents by the number of documentscontaining the term, and then taking the logarithm of that quotient. Ahigh weight in TF-IDF is reached by a high term frequency in the givendocument and a low document frequency of the term in the wholecollection of documents; the weights hence tend to filter out commonterms. Performing a TF-IDF analysis may improve the efficiency of theindex and prevent false positives. As an example and not by way oflimitation, for a post with the text “I wish I were there. Feeling theFOMO. #anxious,” a TF-IDF analysis may determine that the n-gram “fomo”should be extracted, where it has a high importance within the post. Bycontrast, a TF-IDF analysis may determine that the n-grams “i” and “the”should not be extracted, where these word have a low importance withinthe post (because they are common words in many posts, or communicationsgenerally). Although this disclosure describes calculating particularscores in a particular manner, this disclosure contemplates calculatingany suitable scores in any suitable manner.

In calculating the sentiment-scores for a communication, thesocial-networking system 160 may attempt to match the n-grams of thecommunication with sentiment-words in the sentiment-word index. Asmentioned above, the social-networking system 160 may then score thepost as a whole based on the degree to which the n-grams of thecommunication match sentiment-words. The degree of the match maycorrespond to the number of n-grams that match one or moresentiment-words. As an example and not by way of limitation, if severaln-grams match sentiment-words associated with the sentiment “Happy,”there may be a high degree of matching with that sentiment. The degreeof the match may alternatively or additionally correspond to the qualityof the match between an n-gram and a sentiment-word. As an example andnot by way of limitation, the n-gram “waiting” may be a weak match forthe sentiment-word “fomo,” whereas the n-gram “fomooo” may be a strongmatch for the sentiment-word “fomo.” The degree of the match may alsocorrespond to the extent to which the matching sentiment-word itselfmatches the respective sentiment. As an example and not by way oflimitation, the sentiment-words “anxious” and “fomo” may both match thesentiment “Anxious,” but the social-networking system 160 may determinethat a match with the former sentiment-word may be of a high degree andmay determine that a match with the latter sentiment-word may be of alow degree. In this example, the social-networking system 160 may awarda relatively high sentiment-score for the former and relatively lowsentiment-score for the latter. A single communication may have multiplesentiment-scores. As an example and not by way of limitation, for a postwith the text “looking forward to watching Star Wars today” thesocial-networking system 160 may calculate the followingsentiment-cores: {0.6 optimistic; 0.3 excited; 0.1 militaristic}. Thepresence of different sentiment-words may affect some or all of thesentiment-scores based on the sentiments with which the sentiment-wordsare associated. As an example and not by way of limitation, for amessage with the text “i love to work,” the social-networking system 160may calculate the following sentiment-scores: {0.6 happy; 0.4 tired}. Inthis example, the sentiment-word “love” may have had a positive effecton the sentiment-score for “Happy” and the sentiment-word “work” mayhave had a negative effect on the same sentiment-score, resulting in avalue of 0.6. The converse may have been true for the sentiment-scorefor “Tired,” resulting in a value of 0.4. The social-networking system160 may further refine the sentiment-scores by considering the text of acommunication holistically (e.g., by considering the usage of certainsentiment-words alongside other sentiment-words). As an example and notby way of limitation, for the message with the text “i love to work,”the social-networking system 160 may calculate the followingsentiment-scores: {0.6 happy; 0.3 motivated; 0.1 tired}. In thisexample, the sentiment-score for the sentiment “Motivated” may haveresulted from the placement of the word “love” near the word “work,”which may indicate that the author of the message is feeling motivated.Alternatively, the social-networking system 160 may calculate a highsentiment-score for the sentiment “Sarcastic” because, based on ahistory of communications on the social-networking system 160, thesocial-networking system 160 may conclude that users are often sarcasticwhen they associate work with the word “love.” In particularembodiments, the social-networking system 160 may base its calculationof sentiment-scores on other sentiment indicators. As an example and notby way of limitation, the social-networking system 160 may increase thesentiment-score for the sentiment “Excited” or the sentiment“Frustrated” if it detects the use of an exclamation point. In thisexample, the level of increase may be affected by the number ofexclamation points in the communication (e.g., “can't believe whathappened today!!!!”). As another example and not by way of limitation,the use of an ellipsis may cause the increase of the sentiment-score forthe sentiment “Confused” (e.g., “can't believe what happened today . . ..”). In particular embodiments, the social-networking system 160 maybase its calculation of sentiment-scores on information associated withthe particular content item. As an example and not by way of limitation,the information may include metadata that describes the nature of thecontent. In this example, referencing FIG. 3, metadata for the contentitem 310 may specify that the content item 130 contains sarcasm (e.g.,because the content provider, the television show Last Week Tonight,illustrated in FIG. 3, is a satirical news program). Consequently, thesocial-networking system 160 may determine that a communicationincluding the word “great” should receive a high sentiment-score for thesentiment “Frustrated”—even though “great” may be a sentiment-wordassociated with the sentiment “Excited”—because the term “great” may beused sarcastically in the context of a content item that inspiressarcasm. In particular embodiments, the social-networking system 160 maybase its calculation of sentiment-scores on information associated withthe content-distributor (e.g., a creator of the content item, apublisher of the content item that may have simply published the contentitem without itself creating the content item) associated with theparticular content item. As an example and not by way of limitation, foran article from a satire publication (e.g., The Onion), thesocial-networking system 160 may calculate, for a communication, ahigher sentiment-score for the sentiment “Amused.”

In particular embodiments, the social-networking system 160 maycalculate the sentiment-score of a communication based on a degree towhich an embedding of a sentiment-word matches an embedding of an n-gramof the text of the communication. The social-networking system 160 mayuse one or more features of the deep-learning model described inco-pending U.S. patent application Ser. No. 14/949,436, filed 23 Nov.2015, which is incorporated by reference. The social-networking system160 may embed n-grams (including sentiment-words) in a multi-dimensionalembedding space (e.g., a d-dimensional embedding space). Each n-gram maybe mapped to a respective vector representation using, for example andnot by way of limitation, a dictionary generated by the deep-learningmodel. Each of the vector representations may be a vector

^(d), where

denotes the set of real numbers and d is a hyper-parameter that controlscapacity. Each vector representation for an n-gram may correspond to anembedding in the embedding space. As an example and not by way oflimitation, coordinates for a point in the embedding space may bedetermined based on each vector representation. An embedding for aparticular sentiment-word may be at a location in the embedding spacethat is within a threshold distance of embeddings of relevant n-grams(e.g., n-grams that are frequently used with the sentiment-word, n-gramsthat are pre-determined to be associated with the sentiment-word). Amatch may be found between an embedding of a sentiment-word and anembedding of an n-gram if it is within the threshold distance. As anexample and not by way of limitation, the n-gram “despot” may match thesentiment-word “tyrant” because their respective embeddings may bewithin a threshold distance. In this example, a post with the text “theking is a tyrant” may have a similar sentiment-score to a post with thetext “the king is a despot.” The degree of the match may be based on thedistance between the respective embeddings. As an example and not by wayof limitation, for a post with the text “this is entertaining,” theembedding of the n-gram “entertaining” may be within the thresholddistance of the embedding of the sentiment-word “funny,” but may be moredistant than the embedding of the n-gram “amusing.” In this example, apost with the text “this is amusing” may receive a highersentiment-score for the sentiment “Funny” than the post with the text“this is entertaining.”

In particular embodiments, the calculation of the one or moresentiment-scores for the each of the plurality of communications mayinclude calculating one or more sentiment-scores based on one or moreclassifier functions. Each classifier function may calculate one or moresentiment-scores based on a unique set of information from acommunication to calculate a respective sentiment-score. As an exampleand not by way of limitation, the different sets of information mayinclude any combination of the text of the communication, the titleassociated with the particular content item, the content distributorassociated with the particular content item, metadata associated withthe content item, the text of one or more replies made in response tothe communication, or the nontextual commentary of the communication(e.g., media items such as emojis). In this example, a first classifierfunction may calculate a sentiment-score for a communication based onthe text of the communication; a second classifier function may do sobased on the text of the communication and the title associated with theparticular content item; a third classifier function may do so based onthe text of the communication as well as metadata associated with theparticular content item; a fourth classifier function may do so based onthe text of one or more replies made in response to the communication; afifth classifier function may do so based on the nontextual commentaryof the communication; a sixth classifier function may do so based on asummary of the article provided by the content publisher (e.g., awebsite from the article is linked); and a seventh classifier may do sobased on the identity of the content publisher as determined by thedomain of the link. Each of the classifier functions may be a functionof a series of different elements (e.g., n-grams) of the relevantcommentary in a communication. As an example and not by way oflimitation, a sentiment-score may be calculated using the firstclassifier function as follows: score=f₁(n₁, n₂, . . . , n_(N)), wherethe n series represents n-grams of the text of the communication (i.e.,the relevant commentary for the first classifier function). As anotherexample and not by way of limitation, a sentiment-score may becalculated using the third classifier function as follows: score=f₃ (n₁,n₂, . . . , n_(N), m₁, m₂, . . . , m_(N)), where the n series representsn-grams of the text of the communication and the m series representsn-grams of the metadata associated with the particular content item. Foreach classifier function, one or more sentiment-scores may be determinedbased on the set of information unique to that classifier. As an exampleand not by way of limitation, for a post with the text “oh wow,” usingthe first classifier function, the social-networking system 160 mayextract n-grams from the text of the communication and match them withsentiment-words as discussed above. In this example, the firstclassifier function may return the following sentiment-scores: {0.7excited; 0.3 appreciative}. As another example and not by way oflimitation, for the second classifier function, a communication with thetext “wow” that links to an article titled “Hit and Run on GroveStreet,” may receive a relatively high sentiment-score for the sentiment“Scared,” whereas the same text in a communication linking to an articletitled “Man Does Double Backflip” may receive a relatively highsentiment-score for the sentiment “Impressed.” As another example andnot by way of limitation, for the third classifier function, thesocial-networking system 160 may extract n-grams from the text of thecommunication and may also extract metadata from the content item thatmay specify that the content item is satire. In this example, the thirdclassifier function may return the following sentiment-scores for thesame post as the previous example: {0.5 amused; 0.3 frustrated; 0.2excited}. As yet another example and not by way of limitation, for theseventh classifier function, the social-networking system 160 may append“_dom_{name_of_website}” to the communication, and this may be anembedding for the domain. The social-networking system 160 may use thisas a means for calculating sentiment-scores, with the seventh classifierfunction, for the communication based on the associated domain. As anexample and not by way of limitation, a communication linking to anarticle from a comical website may receive a higher sentiment-score forthe sentiment “Happy” than a communication linking to an article from anews website.

In particular embodiments, the social-networking system 160 maydetermine, for each of the plurality of communications, an overallsentiment for the communication. The overall sentiment for acommunication may be based on the calculated sentiment-scores for thecommunication. In particular embodiments, determining the overallsentiment may include summing different sentiment-scores calculated forthe communication. Different combinations of different sentiment-scoresfor different sentiments may result in different overall sentimentsbased on a suitable algorithm. As an example and not by way oflimitation, the algorithm may determine that the sentiment-scores {0.6angry, 0.4 frustrated} may be summed to result in the overall sentiment“Angry”. The overall sentiment need not be one of the sentimentscorresponding to the calculated sentiment-scores. As an example and notby way of limitation, the algorithm may determine that thesentiment-scores {0.7 optimistic, 0.3 sad} may be summed to result inthe overall sentiment “Motivated.” In particular embodiments, thesocial-networking system 160 may sum the sentiment-scores calculatedused some or all of the different classifier functions. In particularembodiments, the summing of the different sentiment-scores may includeweighting the different sentiment-scores differently. As an example andnot by way of limitation, sentiment-scores calculated using differentclassifier functions may each be weighted. In this example, at leastsome of the sentiment-scores calculated using the different classifierfunctions may be weighted differently (e.g., the first and secondclassifier functions may have a weight of 2.0, while the third, fourth,and fifth classifier functions may have a weight of 0.9). In particularembodiments, the social-networking system 160 may apply a thresholdfilter, which may determine that an overall sentiment cannot be resolvedfor a particular communication. In such a case, the social-networkingsystem 160 may return a null overall sentiment. This may occur whenthere is a threshold degree of contradicting sentiment-scores for thecommunication. As an example and not by way of limitation, if thecommunication receives the sentiment-scores {0.6 happy, 0.4 sad}, thesocial-networking system 160 may return a null overall sentiment (e.g.,if the threshold filter specifies that the maximum sentiment-score for acontradictory sentiment is 0.3). As another example and not by way oflimitation, the same may result if a first classifier function returns asentiment-score of {0.6 happy} and a second classifier function returnsa sentiment-score of {0.4 sad} (e.g., if the threshold filter specifiesthat the maximum sentiment-score for a contradictory sentiment is 0.3).The threshold filter may consider the nature of the different sentimentsin determining whether they are truly contradictory. As an example andnot by way of limitation, the sentiment “Happy” may not necessarilycontradict the sentiment “Optimistic”. Although this disclosuredescribes determining a particular sentiment in a particular manner,this disclosure contemplates determining any suitable sentiment in anysuitable manner.

In particular embodiments, the social-networking system 160 maycalculate one or more sentiment levels for the particular content item.Each sentiment level may correspond to a sentiment. A sentiment levelfor a particular sentiment may be calculated based on a total number ofcommunications determined to have the particular sentiment as an overallsentiment. In particular embodiments, the total number of communicationsmay be a global total (e.g., all users of the online social network).Alternatively, the total number of communications may be a total numberof communications authored by users having one or more user-attributescorresponding to a particular user-attribute specified by, or shared by,a particular user. As an example and not by way of limitation, theparticular user may be a user for whom the sentiments-module is beinggenerated. In this example, the total number of communications may be anumber of communications authored by users of the online social networkwithin a particular geographical region, all users within a particulardemographic. In the same example, the total number of communications maybe a number of communications authored by social connections (e.g., onthe online social network) of the particular user. In particularembodiments, the social-networking system 160 may determine ageo-location associated with the client system 130 of the user and limitthe total number of communications to include only communications withina geographical region based on the geo-location of the client system130. As an example and not by way of limitation, the social-networkingsystem 160 may determine that the client system 130 is located inCalifornia, and may calculate the total number of communications basedon the number of communications originating in the United States, inCalifornia, in a fifteen-mile radius, or in any other suitablegeographical region. In particular embodiments, the total number ofcommunications may only include certain types of communications. As anexample and not by way of limitation, the total number of communicationsmay include posts, reshares, and comments, but not private messages. Inparticular embodiments, the sentiment level may be calculated based on anumber of associated social signals (e.g., likes, reshares, comments)associated with communications determined to have the respectivesentiment. As an example and not by way of limitation, a communicationwith many likes may increase the calculated sentiment level by a largeramount than a similar communication with fewer likes. In particularembodiments, the sentiment level may be calculated based on a degree ofcertainty that communications are associated with the respectivesentiment. As an example and not by way of limitation, a communicationwith the sentiment-scores {0.9 happy, 0.1 inspired} may increase thecalculated sentiment level of the overall sentiment “Happy” by a largeramount than a communication with the sentiment-scores {0.6 happy, 0.4inspired}. In particular embodiments, the calculated sentiment level maybe a function of any combination of the factors described above or anyother suitable factor on which the sentiment level may be based. As anexample and not by way of limitation, the function for calculating asentiment level may be represented by the following expression:f(m₁,m₂,m₃), where m₁, m₂, and m₃ are three different factors. Thecalculated sentiment level may alternatively be a sum of differentfunctions that may be weighted in a suitable manner (e.g., the weightsbeing pre-determined by the social-networking system 160). As an exampleand not by way of limitation, the function for calculating a sentimentlevel may be represented by the following expression: A f₁(m₁,m₂)+Bf₂(m₃), where m₁, m₂, and m₃ are three different factors, and where Aand B are two different weights. Although this disclosure describescalculating a particular sentiment level in a particular manner, thisdisclosure contemplates calculating any similarly suitable value in anysuitable manner.

In particular embodiments, the social-networking system 160 may generatea sentiments-module that includes one or more sentiment-representationscorresponding to one or more overall sentiments having sentiment levelsgreater than a threshold sentiment level. Each of thesentiment-representations may visually depict the respective sentiment.As an example and not by way of limitation, referencing FIG. 3, thesentiment-representation 360 may be a visual depiction of the sentiment“Motivated.” A threshold sentiment level may be a threshold value, suchthat sentiments having a sentiment level greater than the thresholdvalue may be included in the sentiments-module. Alternatively, thesentiment level and the threshold sentiment level may be a sentimentrank and a threshold sentiment rank, respectively. As an example and notby way of limitation, referencing FIG. 3, threshold sentiment rank mayhave been set to three. In this example, the top three sentiments (i.e.,the sentiments ranked 1 to 3) may have been “Angry”, “Shocked”, and“Motivated,” and may thus have caused the social-networking system 160to include sentiment-representations of those three sentiments in thesentiments-module 320. In particular embodiments, the sentiments-modulemay only be generated for the particular content item if there exist athreshold number of communications (e.g., posts, reshares, comments, ormessages associated with the particular content item) on the onlinesocial network for which an overall sentiment has been determined. As anexample and not by way of limitation, the number of communications meetsa threshold number 2,000 but there may be one communication for which anoverall sentiment cannot be determined (e.g., if it has contradictorysentiment-scores such as {0.5 happy; 0.5 sad} such that the thresholdfilter does not permit an overall sentiment to be determined). In thisexample, the social-networking system 160 may determine that thethreshold number of communications has not been met, and mayconsequently not generate the sentiments-module for the particularcontent item. In particular embodiments, the sentiments-module may begenerated in response to a search query associated with the particularcontent item. In particular embodiments, the sentiments-module may becreated when a search-results interface is to be rendered for theparticular content item. Although this disclosure describes generating aparticular module in a particular manner, this disclosure contemplatesgenerating any suitable module in any suitable manner.

In particular embodiments, the social-networking system 160 may send thesentiments-module to a client system 130 of a user (e.g., a user of theonline social network). As an example and not by way of limitation, thesentiments-module may be sent as a module included in a search-resultsinterface, trending-topics interface, or another suitable search-resultsinterface. The sentiments-module may include, in addition to the one ormore sentiment-representations of overall sentiments, one or morenumerical or other representations of data related to communicationsdetermined to have the represented overall sentiments. As an example andnot by way of limitation, referencing FIG. 3, a numerical representationof the total number of communications (e.g., posts) determined to havethe “Angry” sentiment (e.g., “6,595”) is displayed under thesentiment-representation of that sentiment. As another example and notby way of limitation, a histogram representation of the same informationmay be displayed under the respective sentiments. Although thisdisclosure describes sending a particular module in a particular manner,this disclosure contemplates sending any suitable module or interface inany suitable manner.

In particular embodiments, the social-networking system 160 may receivean input from the client system 130 that specifies a particularsentiment. As an example and not by way of limitation, the particularsentiment may be one of the sentiments represented in thesentiments-module (e.g., referencing FIG. 3, the sentiments “Angry”,“Shocked”, and “Motivated”). A particular sentiment may be specified inany suitable manner. As an example and not by way of limitation,referencing FIG. 3, the sentiment-representations of the three displayedsentiments may be interactive elements (e.g., thesentiment-representation 360), in which case, the user may specify aparticular sentiment by selecting the associatedsentiment-representation. As another example and not by way oflimitation, the user may specify the particular sentiment by selectingit from a dropdown menu. In response to receiving the input, thesocial-networking system 160 may determine one or more communicationsassociated with the particular sentiment. The determined communicationsmay be limited to communications that are viewable to the usersubmitting the input, based on privacy settings associated with thecommunications. As an example and not by way of limitation, an author ofa communication may have set privacy settings for the communicationspecifying that only first-degree connections of the author (e.g., onthe online social network) may view the communication. In this example,the communication may only be viewable to the user if the user is afirst-degree connection of the author. The social-networking system 160may send some or all of the determined communications to the clientsystem 130 of the user. The sent communications may be displayed to theuser alongside the sentiments-module or within a new window, pop-upnotification, or other suitable location.

FIG. 9 illustrates an example method 900 for generating asentiments-module. The method may begin at step 910, where thesocial-networking system 160 may access a plurality of communicationsauthored by one or more users of an online social network, eachcommunication being associated with a particular content item andcomprising a text of the communication. At step 920, thesocial-networking system 160 may calculate, for each of the plurality ofcommunications, one or more sentiment-scores corresponding to one ormore sentiments, respectively, wherein each sentiment-score is based ona degree to which one or more n-grams of the text of the communicationmatch one or more sentiment-words associated with the one or moresentiments. At step 930, the social-networking system 160 may determine,for each of the plurality of communications, an overall sentiment forthe communication based on the calculated sentiment-scores for thecommunication. At step 940, the social-networking system 160 maycalculate one or more sentiment levels for the particular content itemcorresponding to one or more sentiments, respectively, each sentimentlevel being based on a total number of communications determined to havethe overall sentiment of the sentiment level. At step 950, thesocial-networking system 160 may generate a sentiments-module comprisingone or more sentiment-representations corresponding to one or moreoverall sentiments having sentiment levels greater than a thresholdsentiment level. Particular embodiments may repeat one or more steps ofthe method of FIG. 9, where appropriate. Although this disclosuredescribes and illustrates particular steps of the method of FIG. 9 asoccurring in a particular order, this disclosure contemplates anysuitable steps of the method of FIG. 9 occurring in any suitable order.Moreover, although this disclosure describes and illustrates an examplemethod for generating a sentiments-module including the particular stepsof the method of FIG. 9, this disclosure contemplates any suitablemethod for sending data corresponding to media items based on a userinput including any suitable steps, which may include all, some, or noneof the steps of the method of FIG. 9, where appropriate. Furthermore,although this disclosure describes and illustrates particularcomponents, devices, or systems carrying out particular steps of themethod of FIG. 9, this disclosure contemplates any suitable combinationof any suitable components, devices, or systems carrying out anysuitable steps of the method of FIG. 9.

Quotations-Modules

In particular embodiments, the social-networking system may generate aquotations-module for a search-results interface. As discussedpreviously, the social-networking system 160 may access a plurality ofcommunications authored by one or more users of an online socialnetwork. Each of the plurality of communications may be associated witha particular content item and may include some form of commentary, whichmay include a text of the communication. Although this disclosurefocuses on textual commentary, it contemplates other forms ofcommentary, which may include media items (e.g., emojis, stickers, imagefiles, audio files, video files), which the social-networking system 160may interpret into text. For each of the plurality of communications,the social-networking system 160 may extract one or more quotations fromthe text of the communication. A quotation may be identified, forexample, by quotation-indicators such as quotation marks, or bycomparing the text of the communication with text from the particularcontent item. The social-networking system 160 may determine, for eachextracted quotation, one or more partitions of the quotation. As anexample and not by way of limitation, a quotation may be partitionedinto individual sentences or clauses. The social-networking system 160may group the extracted quotations into one or more clusters based on arespective degree of similarity among their respective one or morepartitions. As an example and not by way of limitation, thesocial-networking system may calculate a degree of similarity based on aLevenshtein distance between two quotations and may group the twoquotations together if they are within a threshold degree of similarity.The social-networking system 160 may calculate a cluster-score for eachcluster based on a frequency of occurrence of one or more partitions ofone or more quotations in the cluster in communications associated withthe particular content item. The social-networking system 160 maygenerate a quotations-module comprising one or more representativequotations, each representative quotation being a quotation from acluster having a cluster-score greater than a threshold cluster-score.The quotations-module may allow a user who is interested in theparticular content item to quickly view popular quotations from theparticular content item. This may help the user understand theparticular content item better (e.g., by focusing the user's attentionon particularly important or relevant portions of the particular contentitem) or may help the user understand how others view the particularcontent item (e.g., by displaying what other users felt were importantenough to quote in a communication). Although this disclosure focuses ondisplaying quotations within a module, it contemplates otherapplications for the extracted quotations. As an example and not by wayof limitation, quotations may be displayed as search results outside ofany particular module. As another example and not by way of limitation,adhering to privacy constraints (e.g., a user may opt in or opt out ofhaving quotations or other information associated with the user used inthe online social network for any or all purposes), the quotations andinformation related to the users (e.g., demographic information) whoauthored the communications may be compiled into a report for interestedparties (e.g., a company may study such a report to create quotablecontent for their future press releases that may be targeted toparticular demographics).

In particular embodiments, the social-networking system 160 may access aplurality of communications authored by one or more users of an onlinesocial network. Each communication may be associated with a particularcontent item. The communications may include commentary such as text. Asdiscussed in further detail above, communications may also include mediaitems, and the social-networking system 160 may, in particularembodiments, interpret the media items, translating them into n-grams.Although this disclosure describes accessing particular communicationsin a particular manner, this disclosure contemplates sending anysuitable communications in any suitable manner.

In particular embodiments, the social-networking system 160 may extract,for each of the plurality of communications, one or more quotations fromthe text of the communication. The extracted one or more quotations maybe indexed or otherwise stored in a data store (e.g. the data store164). Alternatively, the communication containing the extracted one ormore quotations may itself may be indexed or stored in a data store(e.g. the data store 164), along with quotation-information specifyingwhere the quotation is within the communication. The social-networkingsystem 160 may retrieve the stored quotations or communications (fromwhich quotations can be reconstructed based on thequotation-information) at any time for generating and displaying thequotations-module, in accordance with the methods described below. Aquotation may be defined to include a text string in the communicationthat is identical or substantially identical to a text string present inthe particular content item. The social-networking system 160 maydetermine that a text string from a communication is a quotation to bethat is to be extracted by comparing the text string to text associatedwith the particular content item and extracting the text string if thereis a match. Text associated with the particular content item may bedefined to include text within the particular content item (e.g., textfrom an article). In so doing, the social-networking system 160 mayconsider text from the particular content item's metadata or othersuitable associated information. As an example and not by way oflimitation, for a particular content item that is an audio file of asong, the social-networking system 160 may compare text strings in acommunication with text strings in associated song-lyric information.The comparison function described above may be performed using analgorithm such as the SimHash algorithm, which is an algorithm that maybe used to compare datasets quickly and efficiently. This process may beuseful for real-time comparisons, where time and space are particularlylimited. The SimHash algorithm may be used to find a similarity betweendatasets. Instead of comparing two datasets, which may be a difficult ortime-consuming task, the SimHash algorithm compares SimHashes of thedatasets. A SimHash of a dataset may be characterized as anapproximation of the dataset (which may be analogized to a fingerprintof the dataset). For example, in comparing two datasets A and B,SimHashes may be created for both A and B, respectively. In thisexample, the dataset A may be text associated with the particularcontent item (e.g., the text of a sentence of an article, the text of asubpart of the article, the text of the entire article) and the datasetB may the text associated with a communication (e.g., the text of asentence of a post associated with the article, the text of a subpart ofthe post, the text of the entire post). The SimHash algorithm may thencompute the intersection of the two SimHashes (i.e.,SimHash(A)∩SimHash(B)) to determine whether A and B are similar. TheSimHash algorithm may allow for non-exact matches to be determinedsimilar if they are similar to a threshold degree. For example, if thehamming distance between a text string in dataset A and a text string indataset B is sufficiently small, a non-exact match may be determined bythe social-networking system 160, in which case the social-networkingsystem 160 may extract the corresponding text as a quotation. Inparticular embodiments, if a post has multiple sentences, thiscomparison process may be performed iteratively for each sentence.

Alternatively or additionally, the social-networking system 160 mayextract as quotations text strings in a communication that have beendemarcated as a quotation by the author of the communication using anysuitable quotation-indicator. Quotation-indicators may be languagedevices, such as punctuations that commonly serve to demarcatequotations. As examples and not by way of limitation,quotation-indicators may be include a set of single or double quotationmarks, a set of guillemets, a set of brackets, a colon (e.g., a post mayinclude the text “Neil Armstrong: That's one small step for a man, onegiant leap for mankind,” where the quotation to be extracted is the textfollowing the colon), or any other suitable device. The online socialnetwork may have its own quotation-indicators that allow an author userto designate a text string as a quotation. As an example and not by wayof limitation, an author user may be able to select a text string withina communication and specify that it is a quotation before posting thecommunication. In order to be considered a quotation, a text string mayneed to have a minimum number of n-grams (e.g., three words). Althoughthis disclosure focuses on extracting text, it contemplates extractingother forms of commentary such as media items. As an example and not byway of limitation, an article may contain the following string: “don'tcry because it's over; instead,

because it happened.” In this example, the emoji “

” may have meaning within the context of the string (e.g., it may standfor the word “smile”), and a communication may include the emoji whenquoting the string from the article. The social-networking system 160may accordingly extract the entire string, including the emoji, as aquotation. Although this disclosure describes extracting particularelements of particular communications in a particular manner, thisdisclosure contemplates extracting any suitable element of any suitablecommunications in any suitable manner.

In particular embodiments, the social-networking system 160 maydetermine for each extracted quotation, one or more partitions of thequotation. The social-networking system 160 may determine any suitablepartitions for a quotation. As an example and not by way of limitation,a quotation that includes more than one sentence may be partitioned intoindividual sentences or into groups of sentences. As another example, aquotation may be partitioned into individual clauses or groups ofclauses. As yet another example, a quotation may be partitioned intoindividual phrases or groups of phrases. In some cases, thesocial-networking system 160 may determine that a quotation has only onepartition. Although this disclosure describes determining particularpartitions in a particular manner, this disclosure contemplatesdetermining any suitable partitions in any suitable manner.

In particular embodiments, the social-networking system 160 may groupthe extracted quotations into one or more clusters based on a respectivedegree of similarity among their respective one or more partitions. Thesocial-networking system 160 may group extracted quotations into acluster if they are within a threshold degree of similarity. The degreeof similarity may be determined based on a calculation of a similaritymetric that measures the similarity between the quotations. As anexample and not by way of limitation, the similarity metric may be basedon a suitable edit distance such as a Levenshtein distance between thepartitions. For example, for two quotations Q1 and Q2, thesocial-networking system 160 may calculate a similarity metric valueusing the following equation:

${{similarity}\mspace{14mu} {metric}} = {1 - {\frac{{Levenshtein}\mspace{14mu} {{Distance}\left( {{Q\; 1},{{intersection}\left( {{Q\; 1},{Q\; 2}} \right)}} \right)}}{{length}\left( {Q\; 1} \right)}.}}$

In this example, Q1 and Q2 may be quotations having different stringlengths (e.g., Q1 may be shorter than Q2). In this example, the closerthe similarity metric is to 1, the more similar Q1 and Q2 are. Twoquotations may be determined to be sufficiently similar to be grouped ina cluster if the similarity metric is sufficiently close to 1. There maybe a minimum similarity metric value, below which two quotations aredeemed not sufficiently similar. In some instances, Q1 and Q2 may bedescribed as having some partitions that overlap and others that do not.As an example and not by way of limitation, Q1 and Q2 may both bequoting a paragraph from an article with sentences 1-7. Q1 may be aquotation consisting of partitions (e.g., in this example, eachpartition corresponding to a respective sentence of the same number)2-5; Q2 may be a quotation consisting of partitions 1-7. In thisexample, the social-networking system 160, may compute for these twoquotations, a similarity metric that indicates that Q1 and Q2 aresufficiently similar. The social-networking system 160 may, as a result,group Q1 and Q2 into a single cluster. The degree of similarity betweentwo quotations may also be based on an edit distance between the twoquotations. Edit distance is a way of quantifying how similar ordissimilar two strings are to one another by counting the minimum numberof operations required to transform one string into the other (e.g., thenumber of characters that need to be changed and the relative positionof the characters). Quotations with a small edit distance may be moresimilar than quotations with a large edit distance. As an example andnot by way of limitation, the quotation “roughly 1.3 billion tonnes offood get wasted every year” may have a small edit distance with thequotation “roughly 1.3 billion tons of food get wasted every year”because only two operations are required to transform “tonnes” into“tons” (a deletion of one “n” and a deletion of the “e” in “tonnes”).The social-networking system 160 may group quotations that are within amaximum edit distance into a single cluster. Although this disclosuredescribes grouping particular elements of particular communications in aparticular manner, this disclosure contemplates grouping any suitableelements of any suitable communications in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a cluster-score, which may be a quantitative measure of therelevance or popularity of a cluster of quotations, for each clusterbased on a frequency of occurrence of one or more quotations in thecluster in communications associated with the particular content item.Referring to the example immediately above, if there are thirtyoccurrences of Q1 and twenty occurrences of Q2 in communications withina period of time, the social-networking system 160 may determine thatthere are fifty occurrences of their respective cluster within theperiod of time. A cluster-score may then be calculated based on thisfrequency of occurrence. Alternatively, the cluster-score may be basedon a frequency of occurrence of one or more partitions of the one ormore quotations in the cluster. The social-networking system 160 maysimilarly calculate cluster-scores for any other clusters. Thecluster-scores may be calculated based on quotations that were extractedover a period of time (e.g., over the past three hours), or mayalternatively be based on an overall count of quotations. In particularembodiments, the cluster-score may be based on a degree of mutualsimilarity of quotations in the cluster. As an example and not by way oflimitation, a cluster with highly similar quotations that occur 30 timesmay receive a higher cluster-score than a cluster with less similarquotations that occur 30 times, all else equal. In particularembodiments, the cluster-score for a particular cluster may be based ona number of social signals associated with quotations in the cluster. Asan example and not by way of limitation, a cluster with quotations fromcommunications having many likes may receive a higher cluster-score thanotherwise. In particular embodiments, the cluster-score may be based ondemographic information associated with a user for whom thequotations-module is generated. As an example and not by way oflimitation, clusters containing quotations from communications byauthors of the same age group or nationality as the user may receive ahigher cluster-score than otherwise. As another example and not by wayof limitation, clusters of quotations including explicit language mayreceive a lower cluster-score than otherwise if the user is under acertain age. In particular embodiments, the cluster-score may be basedon a language associated with a user for whom the quotations-module isgenerated. As an example and not by way of limitation, if the user hasindicated only a knowledge of English (e.g., through profileinformation, device settings, historical usage of the online socialnetwork), a quotation from a Hindi-language portion of the particularcontent item may receive a lower quotation-score than a quotation froman English-language portion of the particular content item. Inparticular embodiments, the cluster-score may be based on userpreferences associated with a user (as determined, for example, by ausage history of the user or by preferences explicitly specified by theuser) for whom the quotations-module is generated. As an example and notby way of limitation, for a user who has a history of reading articleswith relatively simple language, clusters quoting relatively simplelanguage may receive a higher cluster-score than clusters quotingrelatively dense or difficult language. In particular embodiments, thecluster-score may be based on a current geo-location of the clientsystem 130 of a user for whom the quotations-module is generated. As anexample and not by way of limitation, clusters containing quotationsfrom communications originating from within a region near thegeo-location of the client system 130 of the user may receive a highercluster-score than otherwise. In particular embodiments, thequotation-score for a particular quotation may be based on an affinitycoefficient between a user for whom the quotations-module is generatedand authors of the communications from which quotations in the clusterwere extracted. As an example and not by way of limitation, clusters ofquotations extracted from posts authored by members of a left-leaningpolitical party may receive a higher quotation-score than otherwise ifthe user is also a member of the same political party. In particularembodiments, the calculated cluster-score may be a function of anycombination of the factors described above or any other suitable factoron which the cluster-score may be based. As an example and not by way oflimitation, the function for calculating a cluster-score may berepresented by the following expression: f(m₁,m₂,m₃), where m₁, m₂, andm₃ are three different factors. The calculated cluster-score mayalternatively be a sum of different functions that may be weighted in asuitable manner (e.g., the weights being pre-determined by thesocial-networking system 160). As an example and not by way oflimitation, the function for calculating a cluster-score may berepresented by the following expression: A f₁(m₁,m₂)+B f₂(m₃), where m₁,m₂, and m₃ are three different factors, and where A and B are twodifferent weights. Although this disclosure describes calculatingparticular scores for particular quotations in a particular manner, thisdisclosure contemplates calculating any suitable scores for any suitablequotations in any suitable manner.

In particular embodiments, the social-networking system 160 may generatea quotations-module comprising one or more representative quotations. Inparticular embodiments, the quotations-module may be generated for aparticular content item when there have been a threshold number ofcommunications for which quotations are determined. In particularembodiments, the quotations-module may be generated in response to asearch query associated with the particular content item. In particularembodiments, the quotations-module may be created when a search-resultsinterface is to be rendered for the particular content item. Each of therepresentative quotations may be a quotation from a cluster having acluster-score greater than a threshold cluster-score. Thesocial-networking system 160 may select a representative quotation bycalculating a quotation-score for each quotation. The social-networkingsystem 160 may select quotations having a quotation-score greater than athreshold quotation-score as representative quotations to send to thequotations-module. In particular embodiments, the quotation-score for aparticular quotation may be based on the number of communications inwhich the particular quotation was included. As an example and not byway of limitation, in calculating the quotation-score, thesocial-networking system 160 may count the number of communicationsincluding the exact quotation. As another example and not by way oflimitation, the social-networking system 160 may use an algorithm thatalso accounts for quotations that are non-exact matches. For example,the social-networking system 160 may base the quotation-score on thevalue resulting from the following expression: q+Σ_(i=0) ^(n)(α×similarity metric), where q is a count of the exact quotation and ais a respective count of non-exact quotations (from i=0 to i=n). In thisexample, counts of non-exact matches (e.g., quotations missing a word orphrase) also impact the quotation-score, with their impact beingweighted by their similarity. As an example, a quotation that is verysimilar (e.g., a quotation with a single misspelled word) may have asimilarity metric approaching 1, in which case its count may be weightedin full, whereas a quotation that is very different (e.g., a quotationmissing a sentence) may have a similarity metric of 0.5, in which caseits count may be weighted in half. In particular embodiments, thesocial-networking system 160 may update the quotations-module inreal-time. The social-networking system 160 may ensure efficient usageof space and resources by limiting the number of quotations stored at agiven time, performing the steps above and pushing the resultingrepresentative quotations periodically, or at a point when there existsa need for an update. In particular embodiments, the quotation-score fora particular quotation may be based on the string length of theparticular quotation. As an example and not by way of limitation, theremay be an optimal string length for quotations displayed in thequotations-module. In this example, quotations that have a string lengthclose to the optimal string length may receive a higher quotation-scorethan otherwise. In particular embodiments, the quotation-score for aparticular quotation may be based on a number of social signalsassociated with the particular quotation. As an example and not by wayof limitation, a quotation consisting of partitions 2-5 may have beenincluded in ten communications that each have two likes. In thisexample, the particular quotation may be deemed to have twenty likes intotal, and may result in the social-networking system 160 calculating ahigher quotation-score for the particular quotation that it would havehad if the particular quotation only had fifteen likes. In particularembodiments, the quotation-score for a particular quotation may be basedon a quality of the particular quotation. As an example and not by wayof limitation, the particular quotation may receive a lowerquotation-score if it has spelling errors, has nonconventionalcapitalization, is written in all capitals, or has other undesirablequalities. In particular embodiments, the quotation-score for aparticular quotation may be based on a the presence of certain words inthe particular quotation. As an example and not by way of limitation,the social-networking system 160 may consider information associatedwith a user for whom the quotations-module is generated to calculatehigher quotation-scores for quotations that censor inappropriate words.For example, in the case of a five-year-old user, the quotation-scorefor “turn that **** thing off” may receive a higher quotation-score than“turn that damn thing off.” In particular embodiments, thequotation-score for a particular quotation may be based on informationassociated with one or more users who authored the communicationsincluding the particular quotation. As an example and not by way oflimitation, the information may include demographic information. In thisexample, the social-networking system 160 may compare the demographicinformation of the author users with the demographic information of auser for whom the quotations-module is generated. For example, for aquotations-module generated for a user who is thirty years old, theparticular quotation may have a higher quotation-score if it werecommonly quoted in communications authored by users in their thirtiesthan by users in their sixties. As another example and not by way oflimitation, the information may include location information. Forexample, for a quotations-module generated for a user who is in theUnited States, the particular quotation may have a higherquotation-score if it were commonly quoted in communications authored byusers in the United States (as determine by, for example, geo-locationinformation associated with the author users' client systems) than byusers in Canada. In particular embodiments, the calculatedquotation-score may be a function of any combination of the factorsdescribed above or any other suitable factor on which thequotation-score may be based. As an example and not by way oflimitation, the function for calculating a quotation-score may berepresented by the following expression: f(m₁,m₂,m₃), where m₁, m₂, andm₃ are three different factors. The calculated quotation-score mayalternatively be a sum of different functions that may be weighted in asuitable manner (e.g., the weights being pre-determined by thesocial-networking system 160). As an example and not by way oflimitation, the function for calculating a quotation-score may berepresented by the following expression: A f₁(m₁,m₂)+B f₂(m₃), where m₁,m₂, and m₃ are three different factors, and where A and B are twodifferent weights. Although this disclosure describes generating aparticular module that includes particular elements of particularcommunications, this disclosure contemplates generating, in any suitablemanner, any suitable module or interface that includes any suitableelements of any suitable communications.

In particular embodiments, the social-networking system 160 may send,for display, the quotations-module to a client system 130 of a user(e.g., a user of the online social network). As an example and not byway of limitation, referencing FIG. 3, the quotations-module 330 may besent as a module included in a search-results interface. Thementions-module may also be sent as a module included in any othersuitable search-results interface, such as a trending-topics interface.The quotations-module may include, in addition to the representativequotations, one or more numerical or other representations of datarelated to communications from which the representative quotations wereextracted or communications from which quotations of the same clusterwere extracted. As an example and not by way of limitation, a numericalrepresentation of the total number of communications that included arepresentative quotation may be displayed alongside the representativequotation (e.g., referencing the quotations-module 330 in FIG. 3, thetext “28 people quoted this” displayed under the respective quotation).As another example and not by way of limitation, a histogramrepresentation of the same information may be displayed in a similarfashion. Although this disclosure describes sending a particular modulein a particular manner, this disclosure contemplates sending anysuitable module or interface in any suitable manner.

FIG. 10 illustrates an example method 1000 for generating aquotations-module. The method may begin at step 1010, where thesocial-networking system 160 may access a plurality of communicationsauthored by one or more users of an online social network, eachcommunication being associated with a particular content item andcomprising a text of the communication. At step 1020, thesocial-networking system 160 may extract, for each of the plurality ofcommunications, one or more quotations from the text of thecommunication. At step 1030, the social-networking system 160 maydetermine, for each extracted quotation, one or more partitions of thequotation. At step 1040, the social-networking system 160 may group theextracted quotations into one or more clusters based on a respectivedegree of similarity among their respective one or more partitions. Atstep 1050, the social-networking system 160 may calculate acluster-score for each cluster based on a frequency of occurrence of oneor more partitions of one or more quotations in the cluster incommunications associated with the particular content item. At step1060, the social-networking system 160 may generate a quotations-modulecomprising one or more representative quotations, each representativequotation being a quotation from a cluster having a cluster-scoregreater than a threshold cluster-score. Particular embodiments mayrepeat one or more steps of the method of FIG. 10, where appropriate.Although this disclosure describes and illustrates particular steps ofthe method of FIG. 10 as occurring in a particular order, thisdisclosure contemplates any suitable steps of the method of FIG. 10occurring in any suitable order. Moreover, although this disclosuredescribes and illustrates an example method for generating aquotations-module including the particular steps of the method of FIG.10, this disclosure contemplates any suitable method for sending datacorresponding to media items based on a user input including anysuitable steps, which may include all, some, or none of the steps of themethod of FIG. 10, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 10, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 10.

Mentions-Modules

In particular embodiments, the social-networking system may generate amentions-module for a search-results interface. As discussed previously,the social-networking system 160 may access a plurality ofcommunications authored by one or more users of an online socialnetwork. Each of the communications may be associated with a particularcontent item. The social-networking system 160 may extract, for each ofthe plurality of communications, one or more n-grams from the text ofthe communication. Although this disclosure focuses on textualcommentary, it contemplates other forms of commentary, which may includemedia items (e.g., emojis, stickers, image files, audio files, videofiles), which the social-networking system 160 may interpret into text.The social-networking system 160 may identify one or more mention-termsfrom the one or more extracted n-grams. Each mention-term may be definedto include a noun-phrase. The social-networking system 160 may confirmthat the noun-phrase is not a quotation from the particular content item(e.g., by comparing it against the text of quotations in thequotations-module or against the text of the particular content itemitself). The social-networking system 160 may calculate a term-score foreach mention-term. The term-score may be based on a frequency ofoccurrence of the mention-term in the plurality of communications. As anexample and not by way of limitation, a mention-term that was includedin fifty of the plurality of communications associated with theparticular content item may receive a greater term-score than amention-term that was included in forty of the same set ofcommunications. The social-networking system 160 may generate amentions-module including one or more mentions. Each of the mentions inthe mentions-module may include a mention-term having a term-scoregreater than a threshold term-score. Each of the mentions may also havetext from one or more communications that include the mention-term. Asan example and not by way of limitation, a mention may include at leasta portion of the text of a communication that includes the mention-term,including text that immediately precedes or follows the mention-term.The mention-term may be in bold, highlighted, or otherwise distinguishedfrom the rest of the included text. In the aggregate, people react tocontent items in similar ways such that particular n-grams may emerge ascommonly used in communications relating to the content items. Thesecommonly used n-grams can be used to summarize the content items, andwhen the n-grams are provided in context with users' communications, canbe used to showcase user reactions in a useful manner. For example,viewing the mentions-module may allow a user who is interested in aparticular content item to better understand the particular content item(e.g., by displaying words that others may have used to describe thecontent item) or to understand how others view the particular contentitem (e.g., by displaying words that may have been used by others todescribe their reaction to the particular content item). Although thisdisclosure focuses on displaying mentions and mention-terms within amodule, it contemplates other applications for the determined mentionsand mention-terms. As an example and not by way of limitation, mentionsmay be displayed as search results outside of any particular module(e.g., a user may search “barack obama” and get a list of mention-termssuch as “state of the union” and “signs ObamaCare”). As another exampleand not by way of limitation, adhering to privacy constraints, thementions or mention-terms and information related to the users (e.g.,demographic information) who authored the communications may be compiledinto a report for interested parties (e.g., a polling company may use itas polling data to understand how particular demographics view apolitical candidate or to understand what campaign slogans resonate withpotential voters).

In particular embodiments, the social-networking system 160 may access aplurality of communications authored by one or more users of an onlinesocial network. Each of the communications may be associated with aparticular content item. As discussed in further detail above,communications may also include media items, and the social-networkingsystem 160 may, in particular embodiments, interpret the media items,translating them into n-grams. Although this disclosure describesaccessing particular communications in a particular manner, thisdisclosure contemplates sending any suitable communications in anysuitable manner.

In particular embodiments, the social-networking system 160 may extract,for each of the plurality of communications, one or more n-grams fromthe text of the communication. Although this disclosure focuses onextracting n-grams, it contemplates extracting other forms of commentarysuch as media items. As an example and not by way of limitation, acommunication may contain the following text: “don't cry because it'sover; instead,

because it happened.” In this example, the emoji “

” may have meaning within the context of the text (e.g., it may standfor the word “smile”), and the social-networking system 160 may extractthe string “

because it happened.”

In particular embodiments, the social-networking system 160 may identifyone or more mention-terms from the one or more extracted n-grams. Amention-term may be defined to include a noun-phrase, or one or moreother suitable n-grams. As an example and not by way of limitation,referencing FIG. 3, as may be seen within the mentions-module 340, thesocial-networking system 160 may have identified, among others, thenoun-phrases “much food” and “grocery stores.” In identifyingmention-terms, the social-networking system 160 may parse through then-grams in the plurality of communications. The social-networking system160 may perform a part-of-speech analysis on the n-grams, and may use anoun-phrase extractor algorithm to identify noun-phrases that may bepotential mention-terms. Alternatively or additionally, thesocial-networking system 160 may determine that one or more n-grams arecommonly used within the plurality of communications and maycorrespondingly determine that they are mention-terms (irrespective ofwhether they are noun-phrases). Alternatively or additionally, thesocial-networking system 160 may compare n-grams with a pre-determinedset of words to determine whether any n-gram is particularly relevant tothe particular content item. The pre-determined set of words may bepopulated using information extracted from the particular content item.As an example and not by way of limitation, a pre-determined set ofwords for an article about food waste may include frequently used wordsthat were extracted from the article, such as “waste” or “1.3 billiontonnes” (which may have been an estimate of how much food is wasted, asreported in the article). More information on identifying n-grams suchas the mention-terms described herein may be found in U.S. patentapplication Ser. No. 14/797,819, filed 13 Jul. 2015.

In particular embodiments, the social-networking system 160 may filterout potential mention-terms (e.g., noun-phrases) based on a relevance ofthe potential mention-terms with respect to the particular content item.A potential mention-term may be of higher relevance if it is unique tothe particular content item as compared with other content items. Thesocial-networking system 160 may use a TF-IDF filter (using the sameTF-IDF principles described in detail above) to filter out potentialmention-terms that are not unique enough to be mention-terms for theparticular content item. At least conceptually, the plurality ofcommunications associated with the particular content item may beanalogized to a document (the social-networking system 160 may eventreat the plurality of communications as a document by storing withinsuch a construct). Potential mention-terms that occur frequently in allcommunications generally, but do not appear frequently enough in theplurality of communications associated with the particular content item,may be filtered out for being not particularly unique or relevant to theparticular content item. As an example and not by way of limitation, apotential mention-term “my” may be filtered out. The social-networkingsystem 160 may further refine this process by analyzing relatedcollections of content items. As an example and not by way oflimitation, the social-networking system 160 may analyze communicationsassociated with other content items within a corpus of communicationsdefined by a shared topic. In this example, for a plurality ofcommunications associated with a content item about Donald Trump's hair,the social-networking system 160 may perform a TF-IDF analysis using thecorpus of all communications that are associated with the topic “DonaldTrump.” Having performed this analysis, the social-networking system 160may determine that the potential mention-term “donald” appearsfrequently in the entire corpus such that it is not particularly uniqueor relevant to the content item about Donald Trump's hair. By contrast,social-networking system 160 may determine that the potentialmention-term “great hair” is sufficiently unique to be identified as amention-term. More information on filtering, including some additionalmethods, may be found in U.S. patent application Ser. No. 14/938,685,filed 11 Nov. 2015, which is incorporated by reference.

In particular embodiments, the social-networking system 160 maycalculate a term-score for each mention-term. The term-score may bebased on a frequency of occurrence of the mention-term in the pluralityof communications. As an example and not by way of limitation, amention-term that occurs fifty times may receive a higher term-scorethan if it had only occurred forty times. Term-scores may be calculatedbased on mention-terms that were present in communications over a periodof time (e.g., over the past three hours), or may alternatively be basedon an overall count of mention-terms. In particular embodiments, theterm-score may be based on social signals associated with communicationsincluding the mention-term. As an example and not by way of limitation,a mention-term identified in communications having many likes mayreceive a higher term-score than otherwise. In particular embodiments,the term-score may be based on demographic information associated withone or more authors of one or more communications including themention-term. As an example and not by way of limitation, the term-scoremay be higher if the communications in which the mention-term wasincluded were authored by users of a particular demographic (e.g., userswho were of the same age as a user for whom the mentions-module isgenerated). As another example and not by way of limitation,mention-terms that include British slang may receive a higher term-scorefor a user from the United Kingdom (e.g., a user whose profileinformation indicates London as a hometown) than for a user from theUnited States. As yet another example and not by way of limitation,mention-terms including explicit language may receive a lower term-scorethan otherwise if the user is under a certain age. In particularembodiments, the term-score may be based on location informationassociated with the client system 130 of a user for whom thementions-module is generated. As an example and not by way oflimitation, the term-score may be higher if the communications in whichthe mention-term was included originated from the same region as theuser for whom the mentions-module is generated, as may be determined bygeo-location information sent from the client system 130 of the user. Inparticular embodiments, the term-score may be based on a languageassociated with a user for whom the mentions-module is generated. As anexample and not by way of limitation, if the user has indicated only aknowledge of Spanish (e.g., through profile information, devicesettings, historical usage of the online social network), a Russianmention-term may receive a lower term-score than an otherwise equivalentSpanish mention-term. In particular embodiments, the term-score for aparticular mention-term may be based on an affinity coefficient betweena user for whom the mentions-module is generated and authors of thecommunications from which the mention-term was identified. As an exampleand not by way of limitation, mention-terms extracted from postsauthored by members of a right-leaning political party may receive ahigher term-score than otherwise if the user is also a member of thesame political party. In particular embodiments, the term-score for aparticular mention-term is further based on whether the mention-term isincluded in one or more other search-results modules generated for theparticular content item. This may only occur if the one or more othersearch-results modules is, or will be, concurrently displayed with thementions-module. As an example and not by way of limitation, thesocial-networking system 160 may compare the text of quotations in aquotations-module with the text of communications surrounding themention-term to see if there is a sufficient degree of matching betweenthe two texts. This may be advantageous for several reasons. Forexample, it may favor original user reactions over mere quotations. Itmay also serve to provide more information to the user by acting as ade-duplication tool (e.g., if a quotation already relates to aparticular mention-term, it may be less likely that the mention-termwill provide new information to the user). Alternatively oradditionally, the social-networking system 160 may achieve the sameresult without having to resort to comparing mention-terms against othersearch-results modules. As an example and not by way of limitation,rather than comparing mention-terms against the text of quotations inthe quotations-module, the social-networking system 160 may use theSimHash algorithm method described above to independently compare a textstring associated with the particular content item with a particulartext string of the communication. In this example, if the text stringsare sufficiently similar (i.e., if they are likely to be quotations),mention-terms identified from the particular text string of thecommunication may receive a relatively low term-score (or alternatively,such mention-terms may be dropped as potential mention-terms entirely).In particular embodiments, the calculated term-score may be a functionof any combination of the factors described above or any other suitablefactor on which the term-score may be based. As an example and not byway of limitation, the function for calculating a term-score may berepresented by the following expression: f(m₁,m₂,m₃), where m₁, m₂, andm₃ are three different factors. The calculated term-score mayalternatively be a sum of different functions that may be weighted in asuitable manner (e.g., the weights being pre-determined by thesocial-networking system 160). As an example and not by way oflimitation, the function for calculating a term-score may be representedby the following expression: A f₁(m₁,m₂)+B f₂(m₃), where m₁, m₂, and m₃are three different factors, and where A and B are two differentweights. Although this disclosure describes calculating particularscores for particular terms in a particular manner, this disclosurecontemplates calculating any suitable scores for any suitable terms inany suitable manner.

In particular embodiments, the social-networking system 160 may generatea mentions-module including one or more mentions. In particularembodiments, the mentions-module may be generated for a particularcontent item when there have been a threshold number of communicationsassociated with the particular content item or when there have been athreshold number of such communications for which mentions aredetermined. In particular embodiments, the mentions-module may begenerated in response to a search query associated with the particularcontent item. In particular embodiments, the mentions-module may becreated when a search-results interface is to be rendered for theparticular content item. Each of the mentions in the mentions-module mayinclude a mention-term having a term-score greater than a thresholdterm-score. The term-score and the threshold term-score may be aterm-rank and a threshold term-rank, respectively. As an example and notby way of limitation, the mentions-module may contain only the top fivementions or mentions including the top five mention-terms. Each of thementions may also have text from one or more communications that includethe mention-term. In particular embodiments, the text from the one ormore communications in each mention is extracted from one or morerepresentative communications from the plurality of communications. Thetext may include one or more n-grams preceding or following the one ormore mention-terms within the representative communication. As anexample and not by way of limitation, referencing FIG. 3, thementions-module 340 includes text from a representative communicationthat precedes (e.g., “I can't even begin to tell how”) and follows(e.g., “gets thrown away at places I've worked”) the mention-term “muchfood.” The mention-term may be in bold (as is the case in thesearch-results module 340 of FIG. 3), highlighted, or otherwisedistinguished from the rest of the included text. In determining therepresentative communications for a particular mention-term, thesocial-networking system 160 may calculate arepresentative-communication-score for communications that include arespective mention-term. A communication that has arepresentative-communication-score greater than a thresholdrepresentative-communication-score may be determined to be arepresentative communication. In particular embodiments, therepresentative-communication-score may be based on an affinitycoefficient between a user for whom the mentions-module is generated andan author of the representative communication. As an example and not byway of limitation, a communication by an author who is a member of thesame group as the user (which may correspondingly increase the affinitycoefficient between the user and the author) may receive a higherrepresentative-communication-score than a similar communication byanother user. As another example and not by way of limitation, acommunication by an author whose posts the user frequently likes (whichmay correspondingly increase the affinity coefficient between the userand the author) may receive a higher representative-communication-scorethan a similar communication by another user. In particular embodiments,the representative-communication-score may be based on an degree ofseparation on the social graph of the online social network between auser for whom the mentions-module is generated and an author of therepresentative communication. As an example and not by way oflimitation, a communication by a first-degree connection of a user mayreceive a higher representative-communication-score than a similarcommunication by a user who is not such a connection. In particularembodiments, the representative-communication-score may be based onsocial signals associated with the communication. As an example and notby way of limitation, a communication with many likes may receive ahigher representative-communication-score than otherwise. In particularembodiments, the representative-communication-score may be based ondemographic information associated with one or more authors of therespective communication. As an example and not by way of limitation,the representative-communication-score may be higher if thecommunication was authored by a user of a particular demographic (e.g.,a user of the same age as a user for whom the mentions-module isgenerated). As another example and not by way of limitation,communications that include Australian slang may receive a higherrepresentative-communication-score for a user from the Australia (e.g.,a user whose profile information indicates Sydney as a hometown) thanfor a user from the United States. As yet another example and not by wayof limitation, communications including explicit language may receive alower representative-communication-score than otherwise if the user isunder a certain age. In particular embodiments, therepresentative-communication-score may be based on location informationassociated with the client system 130 of a user for whom thementions-module is generated. As an example and not by way oflimitation, the representative-communication-score may be higher if therespective communication originated from the same region as the user forwhom the mentions-module is generated, as may be determined bygeo-location information sent from the client system 130 of the user. Inparticular embodiments, the representative-communication-score may bebased on a language associated with a user for whom the mentions-moduleis generated. As an example and not by way of limitation, if the userhas indicated only a knowledge of Arabic (e.g., through profileinformation, device settings, historical usage of the online socialnetwork), a Swahili communication may receive a lowerrepresentative-communication-score than an otherwise equivalent Arabiccommunication. In particular embodiments, therepresentative-communication-score may be based on a user preferenceassociated with a user for whom the mentions-module is generated. As anexample and not by way of limitation, for a user who has a history ofreading articles with relatively simple language, communications withrelatively simple language may receive a higherrepresentative-communication-score than communications quotingrelatively dense or difficult language. In particular embodiments, therepresentative-communication-score may be based on a quality of therespective communication. As an example and not by way of limitation,the particular quotation may receive a lowerrepresentative-communication-score if it has spelling errors, hasnonconventional capitalization, is written in all capitals, or has otherundesirable qualities. In particular embodiments, the calculatedrepresentative-communication-score may be a function of any combinationof the factors described above or any other suitable factor on which therepresentative-communication-score may be based. As an example and notby way of limitation, the function for calculating arepresentative-communication-score may be represented by the followingexpression: f(m₁,m₂,m₃), where m₁, m₂, and m₃ are three differentfactors. The calculated representative-communication-score mayalternatively be a sum of different functions that may be weighted in asuitable manner (e.g., the weights being pre-determined by thesocial-networking system 160). As an example and not by way oflimitation, the function for calculating arepresentative-communication-score may be represented by the followingexpression: A f₁(m₁,m₂)+B f₂(m₃), where m₁, m₂, and m₃ are threedifferent factors, and where A and B are two different weights. Althoughthis disclosure describes generating a particular module that includesparticular elements of particular communications, this disclosurecontemplates generating, in any suitable manner, any suitable module orinterface that includes any suitable elements of any suitablecommunications.

In particular embodiments, the social-networking system 160 may generatethe mentions-module in real-time by running the TF-IDF algorithm inreal-time to calculate term-scores in real-time. This real-timeimplementation may be a leaner version of the method described above,and may result in a quicker and more space-efficient selection of thementions that are to be included in the mentions-module. In thenon-real-time method described above, it was assumed that a large numberof mention-terms could be stored without issue, allowing thesocial-networking system 160 to calculate term-scores for mention-termsidentified over possibly long periods of time. In the real-timeimplementation, there may be space-constraints that limit the number ofmention-terms that may be stored at a given time, such that there may bea maximum-storage-number of mention-terms. As an example and not by wayof limitation, if the maximum-storage-number is set to thirty, onlythirty mention-terms may be stored at a given time for a particularcontent item. In this example, after identifying a new mention-term thatappeared in a single communication, if there are less than thirtymention-terms, the new mention-term may be stored with a respectivecounter set to 1 and a respective decay counter sent to 1. If thesocial-networking system 160 subsequently identifies another occurrenceof the mention-term in a second communication, the respective counterfor the mention-term may be increased by 1, resulting in a count of 2.Following a specified period of time (e.g., 30 seconds), the decaycounters of all stored mention-terms may be increased by 1. Thesocial-networking system 160 may compute the decay of all storedmention-terms. As an example and not by way of limitation, thesocial-networking system 160 may use the following equation to calculatethe decay-value for each mention-term: decay=tf−idfscore×occurrence×r^(d), where tf−idf score is a score based on theoutput of the TF-IDF algorithm, occurrence is the current counter value,r is the decay rate, d is the current decay-counter value. In thisexample, the social-networking system 160 may remove mention-terms thathave a decay-value below a threshold decay-value. The social-networkingsystem 160 may calculate a term-score for each stored mention-term basedon the following equation: term-score=tf−idf score×occurrence. Theterm-score calculated in the real-time implementation may also be basedon any of the factors described above or any suitable combinationthereof and may be used in the same manner described above indetermining which mentions are to be included in the mentions-module.

In particular embodiments, the social-networking system 160 may send,for display, the mentions-module to a client system 130 of a user (e.g.,a user of the online social network). As an example and not by way oflimitation, referencing FIG. 3, the mentions-module 340 may be sent as amodule included in a search-results interface. The mentions-module mayalso be sent as a module included in any other suitable search-resultsinterface, such as a trending-topics interface. The mentions-module mayinclude, in addition to the mention-terms, one or more numerical orother representations of data related to communications that includedthe mention-term. As an example and not by way of limitation, anumerical representation of the total number of communications thatincluded a mention-term may be displayed alongside the mention-term(e.g., referencing the quotations-module 340 in FIG. 3, the text “95people mentioned this” displayed under the respective mention). Asanother example and not by way of limitation, a histogram representationof the same information may be displayed in a similar fashion. Inparticular embodiments, the mentions-module may also include informationabout a user who authored a mention that is displayed in thementions-module. As an example and not by way of limitation, referencingFIG. 3, the mentions-module 340 may include, near each displayedmention, a profile picture of the author of the representativecommunication from which the mention comes from. As another example andnot by way of limitation, the mentions-module 340 may include, near eachdisplayed mention, a user name of the author of the representativecommunication from which the mention comes from. Although thisdisclosure describes sending a particular module in a particular manner,this disclosure contemplates sending any suitable module or interface inany suitable manner.

In particular embodiments, the mention-terms or the mentions may bepurged from the social-networking system 160 (e.g., deleted or otherwiseremoved from computing or storage devices associated with thesocial-networking system) after a pre-defined period of time. Inparticular embodiments, a mention-term may be purged only if the totalnumber of times it has occurred in communications associated with theparticular content item is less than a minimum value. As an example andnot by way of limitation, if the minimum value is ninety and if amention-term has occurred only a total of fifty times, it may be purgedafter a pre-defined period of time (e.g., ninety days). Alternatively oradditionally, a mention or mention-term may be purged only if thefrequency of occurrence of the respective mention-term over a period oftime is less than a minimum value within the plurality of communicationsassociated with the particular content item. As an example and not byway of limitation, if the minimum value is twenty and if a mention-termhas not occurred in twenty communications (or twenty times incommunications) over the past ten days, it may be purged. This purge mayresult in an increase in privacy, a reduction in storage and computationresource requirements, or any other benefits.

FIG. 11 illustrates an example method 1100 for generating amentions-module. The method may begin at step 1110, where thesocial-networking system 160 may access a plurality of communicationsauthored by one or more users of an online social network, eachcommunication being associated with a particular content item andcomprising a text of the communication. At step 1120, thesocial-networking system 160 may extract, for each of the plurality ofcommunications, one or more n-grams from the text of the communication.At step 1130, the social-networking system 160 may identify one or moremention-terms from the one or more extracted n-grams, each mention-termbeing a noun-phrase. At step 1140, the social-networking system 160 maycalculate a term-score for each mention-term based on a frequency ofoccurrence of the mention-term in the plurality of communications. Atstep 1150, the social-networking system 160 may generate amentions-module comprising one or more mentions, each mention comprisinga mention-term having a term-score greater than a threshold term-scoreand text from one or more communications comprising the mention-term.Particular embodiments may repeat one or more steps of the method ofFIG. 11, where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 11 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 11 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method forgenerating a mentions-module including the particular steps of themethod of FIG. 11, this disclosure contemplates any suitable method forsending data corresponding to media items based on a user inputincluding any suitable steps, which may include all, some, or none ofthe steps of the method of FIG. 11, where appropriate. Furthermore,although this disclosure describes and illustrates particularcomponents, devices, or systems carrying out particular steps of themethod of FIG. 11, this disclosure contemplates any suitable combinationof any suitable components, devices, or systems carrying out anysuitable steps of the method of FIG. 11.

Social Graph Affinity and Coefficient

In particular embodiments, the social-networking system 160 maydetermine the social-graph affinity (which may be referred to herein as“affinity”) of various social-graph entities for each other. Affinitymay represent the strength of a relationship or level of interestbetween particular objects associated with the online social network,such as users, concepts, content, actions, advertisements, other objectsassociated with the online social network, or any suitable combinationthereof. Affinity may also be determined with respect to objectsassociated with third-party systems 170 or other suitable systems. Anoverall affinity for a social-graph entity for each user, subjectmatter, or type of content may be established. The overall affinity maychange based on continued monitoring of the actions or relationshipsassociated with the social-graph entity. Although this disclosuredescribes determining particular affinities in a particular manner, thisdisclosure contemplates determining any suitable affinities in anysuitable manner.

In particular embodiments, the social-networking system 160 may measureor quantify social-graph affinity using an affinity coefficient (whichmay be referred to herein as “coefficient”). The coefficient mayrepresent or quantify the strength of a relationship between particularobjects associated with the online social network. The coefficient mayalso represent a probability or function that measures a predictedprobability that a user will perform a particular action based on theuser's interest in the action. In this way, a user's future actions maybe predicted based on the user's prior actions, where the coefficientmay be calculated at least in part on the history of the user's actions.Coefficients may be used to predict any number of actions, which may bewithin or outside of the online social network. As an example and not byway of limitation, these actions may include various types ofcommunications, such as sending messages, posting content, or commentingon content; various types of observation actions, such as accessing orviewing profile interfaces, media, or other suitable content; varioustypes of coincidence information about two or more social-graphentities, such as being in the same group, tagged in the samephotograph, checked-in at the same location, or attending the sameevent; or other suitable actions. Although this disclosure describesmeasuring affinity in a particular manner, this disclosure contemplatesmeasuring affinity in any suitable manner.

In particular embodiments, the social-networking system 160 may use avariety of factors to calculate a coefficient. These factors mayinclude, for example, user actions, types of relationships betweenobjects, location information, other suitable factors, or anycombination thereof. In particular embodiments, different factors may beweighted differently when calculating the coefficient. The weights foreach factor may be static or the weights may change according to, forexample, the user, the type of relationship, the type of action, theuser's location, and so forth. Ratings for the factors may be combinedaccording to their weights to determine an overall coefficient for theuser. As an example and not by way of limitation, particular useractions may be assigned both a rating and a weight while a relationshipassociated with the particular user action is assigned a rating and acorrelating weight (e.g., so the weights total 100%). To calculate thecoefficient of a user towards a particular object, the rating assignedto the user's actions may comprise, for example, 60% of the overallcoefficient, while the relationship between the user and the object maycomprise 40% of the overall coefficient. In particular embodiments, thesocial-networking system 160 may consider a variety of variables whendetermining weights for various factors used to calculate a coefficient,such as, for example, the time since information was accessed, decayfactors, frequency of access, relationship to information orrelationship to the object about which information was accessed,relationship to social-graph entities connected to the object, short- orlong-term averages of user actions, user feedback, other suitablevariables, or any combination thereof. As an example and not by way oflimitation, a coefficient may include a decay factor that causes thestrength of the signal provided by particular actions to decay withtime, such that more recent actions are more relevant when calculatingthe coefficient. The ratings and weights may be continuously updatedbased on continued tracking of the actions upon which the coefficient isbased. Any type of process or algorithm may be employed for assigning,combining, averaging, and so forth the ratings for each factor and theweights assigned to the factors. In particular embodiments, thesocial-networking system 160 may determine coefficients usingmachine-learning algorithms trained on historical actions and past userresponses, or data farmed from users by exposing them to various optionsand measuring responses. Although this disclosure describes calculatingcoefficients in a particular manner, this disclosure contemplatescalculating coefficients in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on a user's actions. The social-networkingsystem 160 may monitor such actions on the online social network, on athird-party system 170, on other suitable systems, or any combinationthereof. Any suitable type of user actions may be tracked or monitored.Typical user actions include viewing profile interfaces, creating orposting content, interacting with content, tagging or being tagged inimages, joining groups, listing and confirming attendance at events,checking-in at locations, liking particular interfaces, creatinginterfaces, and performing other tasks that facilitate social action. Inparticular embodiments, the social-networking system 160 may calculate acoefficient based on the user's actions with particular types ofcontent. The content may be associated with the online social network, athird-party system 170, or another suitable system. The content mayinclude users, profile interfaces, posts, news stories, headlines,instant messages, chat room conversations, emails, advertisements,pictures, video, music, other suitable objects, or any combinationthereof. The social-networking system 160 may analyze a user's actionsto determine whether one or more of the actions indicate an affinity forsubject matter, content, other users, and so forth. As an example andnot by way of limitation, if a user may make frequently posts contentrelated to “coffee” or variants thereof, the social-networking system160 may determine the user has a high coefficient with respect to theconcept “coffee.” Particular actions or types of actions may be assigneda higher weight and/or rating than other actions, which may affect theoverall calculated coefficient. As an example and not by way oflimitation, if a first user emails a second user, the weight or therating for the action may be higher than if the first user simply viewsthe user-profile interface for the second user.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on the type of relationship betweenparticular objects. Referencing the social graph 200, thesocial-networking system 160 may analyze the number and/or type of edges206 connecting particular user nodes 202 and concept nodes 204 whencalculating a coefficient. As an example and not by way of limitation,user nodes 202 that are connected by a spouse-type edge (representingthat the two users are married) may be assigned a higher coefficientthan a user nodes 202 that are connected by a friend-type edge. In otherwords, depending upon the weights assigned to the actions andrelationships for the particular user, the overall affinity may bedetermined to be higher for content about the user's spouse than forcontent about the user's friend. In particular embodiments, therelationships a user has with another object may affect the weightsand/or the ratings of the user's actions with respect to calculating thecoefficient for that object. As an example and not by way of limitation,if a user is tagged in first photo, but merely likes a second photo, thesocial-networking system 160 may determine that the user has a highercoefficient with respect to the first photo than the second photobecause having a tagged-in-type relationship with content may beassigned a higher weight and/or rating than having a like-typerelationship with content. In particular embodiments, thesocial-networking system 160 may calculate a coefficient for a firstuser based on the relationship one or more second users have with aparticular object. In other words, the connections and coefficientsother users have with an object may affect the first user's coefficientfor the object. As an example and not by way of limitation, if a firstuser is connected to or has a high coefficient for one or more secondusers, and those second users are connected to or have a highcoefficient for a particular object, the social-networking system 160may determine that the first user should also have a relatively highcoefficient for the particular object. In particular embodiments, thecoefficient may be based on the degree of separation between particularobjects. The lower coefficient may represent the decreasing likelihoodthat the first user will share an interest in content objects of theuser that is indirectly connected to the first user in the social graph200. As an example and not by way of limitation, social-graph entitiesthat are closer in the social graph 200 (i.e., fewer degrees ofseparation) may have a higher coefficient than entities that are furtherapart in the social graph 200.

In particular embodiments, the social-networking system 160 maycalculate a coefficient based on location information. Objects that aregeographically closer to each other may be considered to be more relatedor of more interest to each other than more distant objects. Inparticular embodiments, the coefficient of a user towards a particularobject may be based on the proximity of the object's location to acurrent location associated with the user (or the location of a clientsystem 130 of the user). A first user may be more interested in otherusers or concepts that are closer to the first user. As an example andnot by way of limitation, if a user is one mile from an airport and twomiles from a gas station, the social-networking system 160 may determinethat the user has a higher coefficient for the airport than the gasstation based on the proximity of the airport to the user.

In particular embodiments, the social-networking system 160 may performparticular actions with respect to a user based on coefficientinformation. Coefficients may be used to predict whether a user willperform a particular action based on the user's interest in the action.A coefficient may be used when generating or presenting any type ofobjects to a user, such as advertisements, search results, news stories,media, messages, notifications, or other suitable objects. Thecoefficient may also be utilized to rank and order such objects, asappropriate. In this way, the social-networking system 160 may provideinformation that is relevant to user's interests and currentcircumstances, increasing the likelihood that they will find suchinformation of interest. In particular embodiments, thesocial-networking system 160 may generate content based on coefficientinformation. Content objects may be provided or selected based oncoefficients specific to a user. As an example and not by way oflimitation, the coefficient may be used to generate media for the user,where the user may be presented with media for which the user has a highoverall coefficient with respect to the media object. As another exampleand not by way of limitation, the coefficient may be used to generateadvertisements for the user, where the user may be presented withadvertisements for which the user has a high overall coefficient withrespect to the advertised object. In particular embodiments, thesocial-networking system 160 may generate search results based oncoefficient information. Search results for a particular user may bescored or ranked based on the coefficient associated with the searchresults with respect to the querying user. As an example and not by wayof limitation, search results corresponding to objects with highercoefficients may be ranked higher on a search-results interface thanresults corresponding to objects having lower coefficients.

In particular embodiments, the social-networking system 160 maycalculate a coefficient in response to a request for a coefficient froma particular system or process. To predict the likely actions a user maytake (or may be the subject of) in a given situation, any process mayrequest a calculated coefficient for a user. The request may alsoinclude a set of weights to use for various factors used to calculatethe coefficient. This request may come from a process running on theonline social network, from a third-party system 170 (e.g., via an APIor other communication channel), or from another suitable system. Inresponse to the request, the social-networking system 160 may calculatethe coefficient (or access the coefficient information if it haspreviously been calculated and stored). In particular embodiments, thesocial-networking system 160 may measure an affinity with respect to aparticular process. Different processes (both internal and external tothe online social network) may request a coefficient for a particularobject or set of objects. The social-networking system 160 may provide ameasure of affinity that is relevant to the particular process thatrequested the measure of affinity. In this way, each process receives ameasure of affinity that is tailored for the different context in whichthe process will use the measure of affinity.

In connection with social-graph affinity and affinity coefficients,particular embodiments may utilize one or more systems, components,elements, functions, methods, operations, or steps disclosed in U.S.patent application Ser. No. 11/503,093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977,027, filed 22 Dec. 2010, U.S. patentapplication Ser. No. 12/978,265, filed 23 Dec. 2010, and U.S. patentapplication Ser. No. 13/632,869, filed 1 Oct. 2012, each of which isincorporated by reference.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, a third-party system 170, a social-networking application, amessaging application, a photo-sharing application, or any othersuitable computing system or application. Although the examplesdiscussed herein are in the context of an online social network, theseprivacy settings may be applied to any other suitable computing system.Privacy settings (or “access settings”) for an object may be stored inany suitable manner, such as, for example, in association with theobject, in an index on an authorization server, in another suitablemanner, or any suitable combination thereof. A privacy setting for anobject may specify how the object (or particular information associatedwith the object) can be accessed, stored, or otherwise used (e.g.,viewed, shared, modified, copied, executed, surfaced, or identified)within the online social network. When privacy settings for an objectallow a particular user or other entity to access that object, theobject may be described as being “visible” with respect to that user orother entity. As an example and not by way of limitation, a user of theonline social network may specify privacy settings for a user-profilepage that identify a set of users that may access work-experienceinformation on the user-profile page, thus excluding other users fromaccessing that information.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photos albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 204 corresponding to a particular photo may havea privacy setting specifying that the photo may be accessed only byusers tagged in the photo and the tagged user's friends. In particularembodiments, privacy settings may allow users to opt in to or opt out ofhaving their content, information, or actions stored/logged by thesocial-networking system 160 or shared with other systems (e.g., athird-party system 170). Although this disclosure describes usingparticular privacy settings in a particular manner, this disclosurecontemplates using any suitable privacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 200. A privacy setting may be specifiedfor one or more edges 206 or edge-types of social graph 200, or withrespect to one or more nodes 202, 204 or node-types of social graph 200.The privacy settings applied to a particular edge 206 connecting twonodes may control whether the relationship between the two entitiescorresponding to the nodes is visible to other users of the onlinesocial network. Similarly, the privacy settings applied to a particularnode may control whether the user or concept corresponding to the nodeis visible to other users of the online social network. As an exampleand not by way of limitation, a first user may share an object to thesocial-networking system 160. The object may be associated with aconcept node 204 connected to a user node 202 of the first user by anedge 206. The first user may specify privacy settings that apply to aparticular edge 206 connecting to the concept node 204 of the object, ormay specify privacy settings that apply to all edges 206 connecting tothe concept node 204. As another example and not by way of limitation,the first user may share a set of objects of a particular object-type(e.g., a set of images). The first user may specify privacy settingswith respect to all objects associated with the first user of thatparticular object-type as having a particular privacy setting (e.g.,specifying that all images posted by the first user are visible only tofriends of the first user and/or users tagged in the images).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degrees-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. In particularembodiments, access or denial of access may be specified by time ordate. As an example and not by way of limitation, a user may specifythat a particular image uploaded by the user is visible to the user'sfriends for the next week. As another example and not by way oflimitation, a company may post content related to a product releaseahead of the official launch, and specify that the content may not bevisible to other users until after the product launch. In particularembodiments, access or denial of access may be specified by geographiclocation. As an example and not by way of limitation, a user may sharean object and specify that only users in the same city may access orview the object. As another example and not by way of limitation, afirst user may share an object and specify that the object is visible tosecond users only while the first user is in a particular location. Ifthe first user leaves the particular location, the object may no longerbe visible to the second users. As another example and not by way oflimitation, a first user may specify that an object is visible only tosecond users within a threshold distance from the first user. If thefirst user subsequently changes location, the original second users withaccess to the object may lose access, while a new group of second usersmay gain access as they come within the threshold distance of the firstuser. Although this disclosure describes particular granularities ofpermitted access or denial of access, this disclosure contemplates anysuitable granularities of permitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom be sent to the user. In the search-query context, an object may beprovided as a search result only if the querying user is authorized toaccess the object, e.g., the privacy settings for the object allow it tobe surfaced to, discovered by, or otherwise visible to the queryinguser. In particular embodiments, an object may represent content that isvisible to a user through a newsfeed of the user. As an example and notby way of limitation, one or more objects may be visible to a user's“Trending” page. In particular embodiments, an object may correspond toa particular user. The object may be content associated with theparticular user, or may be the particular user's account or informationstored on an online social network, or other computing system As anexample and not by way of limitation, a first user may view one or moresecond users of an online social network through a “People You May Know”function of the online social network, or by viewing a list of friendsof the first user. As an example and not by way of limitation, a firstuser may specify that they do not wish to see objects associated with aparticular second user in their newsfeed or friends list. If the privacysettings for the object do not allow it to be surfaced to, discoveredby, or visible to the user, the object may be excluded from the searchresults. Although this disclosure describes enforcing privacy settingsin a particular manner, this disclosure contemplates enforcing privacysettings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users that attend the same university as thefirst user may view the first user's pictures, but that other users thatare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, changes to privacy settings may take effectretroactively, affecting the visibility of objects and content sharedprior to the change. As an example and not by way of limitation, a firstuser may share a first image and specify that the first image is to bepublic to all other users. At a later time, the first user may specifythat any images shared by the first user should be made visible only toa first user group. The social-networking system 160 may determine thatthis privacy setting also applies to the first image and make the firstimage visible only to the first user group. In particular embodiments,the change in privacy settings may take effect only going forward.Continuing the example above, if the first user changes privacy settingsand then shares a second image, the second image may be visible only tothe first user group, but the first image may remain visible to allusers. In particular embodiments, in response to a user action to changea privacy setting, the social-networking system 160 may further promptthe user to indicate whether the user wants to apply the changes to theprivacy setting retroactively. In particular embodiments, a user changeto privacy settings may be a one-off change specific to one object. Inparticular embodiments, a user change to privacy may be a global changefor all objects associated with the user.

In particular embodiments, privacy settings may allow a user to specifywhether particular applications or processes may access, store, or useparticular objects or information associated with the user. The privacysettings may allow users to opt in or opt out of having objects orinformation accessed, stored, or used by specific applications orprocesses. The social-networking system 160 may access such informationin order to provide a particular function or service to the user,without the social-networking system 160 having access to thatinformation for any other purposes. Before accessing, storing, or usingsuch objects or information, the social-networking system 160 may promptthe user to provide privacy settings specifying which applications orprocesses, if any, may access, store, or use the object or informationprior to allowing any such action. As an example and not by way oflimitation, a first user may transmit a message to a second user via anapplication related to the online social network (e.g., a messagingapp), and may specify privacy settings that such messages should not bestored by the social-networking system 160. As another example and notby way of limitation, social-networking system 160 may havefunctionalities that may use as inputs personal or biometric informationof the user. A user may opt to make use of these functionalities toenhance their experience on the online social network. As an example andnot by way of limitation, a user may provide personal or biometricinformation to the social-networking system 160. The user's privacysettings may specify that such information may be used only forparticular processes, such as authentication, and further specify thatsuch information may not be shared with any third-party system 170 orused for other processes or applications associated with thesocial-networking system 160. As yet another example and not by way oflimitation, an online social network may provide functionality for auser to provide voice-print recordings to the online social network. Asan example and not by way of limitation, if a user wishes to utilizethis function of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to send voice messages,to improve voice recognition in order to use voice-operated features ofthe online social network), and further specify that such voicerecording may not be shared with any third-party system 170 or used byother processes or applications associated with the social-networkingsystem 160.

In particular embodiments, privacy settings may allow a user to specifywhether mood or sentiment information associated with the user may bedetermined, and whether particular applications or processes may access,store, or use such information. The privacy settings may allow users toopt in or opt out of having mood or sentiment information accessed,stored, or used by specific applications or processes. Thesocial-networking system 160 may predict or determine a mood orsentiment associated with a user based on, for example, inputs providedby the user and interactions with particular objects, such as pages orcontent viewed by the user, posts or other content uploaded by the user,and interactions with other content of the online social network. Inparticular embodiments, social-networking system 160 may use a user'sprevious activities and calculated moods or sentiments to determine apresent mood or sentiment. A user who wishes to enable thisfunctionality may indicate in their privacy settings that they opt in tosocial-networking system 160 receiving the inputs necessary to determinethe mood or sentiment. As an example and not by way of limitation,social-networking system 160 may determine that a default privacysetting is to not receive any information necessary for determining moodor sentiment until there is an express indication from a user thatsocial-networking system 160 may do so. In particular embodiments,social-networking system 160 may use the predicted mood or sentiment toprovide recommendations or advertisements to the user. In particularembodiments, if a user desires to make use of this function for specificpurposes or applications, additional privacy settings may be specifiedby the user to opt in to using the mood or sentiment information for thespecific purposes or applications. As an example and not by way oflimitation, social-networking system 160 may use the user's mood orsentiment to provide newsfeed items, pages, friends, or advertisementsto a user. The user may specify in their privacy settings thatsocial-networking system 160 may determine the user's mood or sentiment.The user may then be asked to provide additional privacy settings toindicate the purposes for which the user's mood or sentiment may beused. The user may indicate that social-networking system 160 may usehis or her mood or sentiment to provide newsfeed content and recommendpages, but not for recommending friends or advertisements.Social-networking system 160 may then only provide newsfeed content orpages based on user mood or sentiment, and may not use that informationfor any other purpose, even if not expressly prohibited by the privacysettings.

In particular embodiments, the social-networking system 160 maytemporarily access, store, or use particular objects or informationassociated with a user in order to facilitate particular actions of thefirst user, and may subsequently delete the objects or information. Asan example and not by way of limitation, a first user may transmit amessage to a second user, and the social-networking system 160 maytemporarily store the message in a data store 164 until the second userhas view or downloaded the message, at which point the social-networkingsystem 160 may delete the message from the data store 164. As anotherexample and not by way of limitation, continuing with the prior example,the message may be stored for a specified period of time (e.g., 2weeks), after which point the social-networking system 160 may deletethe message from the data store 164. In particular embodiments, a usermay specify whether particular types of objects or informationassociated with the user may be accessed, stored, or used by thesocial-networking system 160. As an example and not by way oflimitation, a user may specify that images sent by the user through thesocial-networking system 160 may not be stored by the social-networkingsystem 160. As another example and not by way of limitation, a firstuser may specify that messages sent from the first user to a particularsecond user may not be stored by the social-networking system 160. Asyet another example and not by way of limitation, a user may specifythat all objects sent via a particular application may be saved by thesocial-networking system 160.

In particular embodiments, privacy settings may allow a user to specifywhether particular objects or information associated with the user maybe accessed from particular client systems 130 or third-party systems170. The privacy settings may allow users to opt in or opt out of havingobjects or information accessed from a particular device (e.g., thephone book on a user's smart phone), from a particular application(e.g., a messaging app), or from a particular system (e.g., an emailserver). The social-networking system 160 may provide default privacysettings with respect to each device, system, or application, and/or theuser may be prompted to specify a particular privacy setting for eachcontext. As an example and not by way of limitation, a user may utilizea location-services feature of the social-networking system 160 toprovide recommendations for restaurants or other places in proximity tothe user. The user's default privacy settings may specify that thesocial-networking system 160 may use location information provided froma client device 130 of the user to provide the location-based services,but that the social-networking system 160 may not store the locationinformation of the user or provide it to any third-party system 170. Theuser may then update the privacy settings to allow location informationto be used by a third-party image-sharing application in order togeo-tag photos.

In particular embodiments, the social-networking system 160 maydetermine that a first user may want to change one or more privacysettings in response to a trigger action associated with the first user.The trigger action may be any suitable action on the online socialnetwork. As an example and not by way of limitation, a trigger actionmay be a change in the relationship between a first and second user ofthe online social network (e.g., “un-friending” a user, changing therelationship status between the users). In particular embodiments, upondetermining that a trigger action has occurred, the social-networkingsystem 160 may prompt the first user to change the privacy settingsregarding the visibility of objects associated with the first user. Theprompt may redirect the first user to a workflow process for editingprivacy settings with respect to one or more entities associated withthe trigger action. The privacy settings associated with the first usermay be changed only in response to an explicit input from the firstuser, and may not be changed without the approval of the first user. Asan example and not by way of limitation, the workflow process mayinclude providing the first user with the current privacy settings withrespect to the second user or to a group of users (e.g., un-tagging thefirst user or second user from particular objects, changing thevisibility of particular objects with respect to the second user orgroup of users), and receiving an indication from the first user tochange the privacy settings based on any of the methods describedherein, or to keep the existing privacy settings.

In particular embodiments, a user may need to provide verification of aprivacy setting before allowing the user to perform particular actionson the online social network, or to provide verification before changinga particular privacy setting. When performing particular actions orchanging particular privacy setting, a prompt may be presented to theuser to remind the user of his or her current privacy settings andasking the user to verify the privacy settings with respect to theparticular action. Furthermore, a user may need to provide confirmation,double-confirmation, authentication, or other suitable types ofverification before proceeding with the particular action, and theaction may not be complete until such verification is provided. As anexample and not by way of limitation, a user's default privacy settingsmay indicate that a person's relationship status is visible to all users(i.e., “public”). However, if the user changes his or her relationshipstatus, the social-networking system 160 may determine that such actionmay be sensitive and may prompt the user to confirm that his or herrelationship status should remain public before proceeding. As anotherexample and not by way of limitation, a user's privacy settings mayspecify that the user's posts are visible only to friends of the user.However, if the user changes the privacy setting for his or her posts tobeing public, the social-networking system 160 may prompt the user witha reminder of that the user's current privacy settings of being visibleonly to friends, and a warning that this change will make all of theusers past posts visible to the public. The user may then be required toprovide a second verification, input authentication credentials, orprovide other types of verification before proceeding with the change inprivacy settings. In particular embodiments, a user may need to provideverification of a privacy setting on a periodic basis. A prompt orreminder may be periodically sent to the user based either on timeelapsed or a number of user actions. As an example and not by way oflimitation, the social-networking system 160 may send a reminder to theuser to confirm his or her privacy settings every six months or afterevery ten photo posts. In particular embodiments, privacy settings mayalso allow users to control access to the objects or information on aper-request basis. As an example and not by way of limitation, thesocial-networking system 160 may notify the user whenever a third-partysystem 170 attempts to access information associated with the user, andrequire the user to provide verification that access should be allowedbefore proceeding.

Systems and Methods

FIG. 12 illustrates an example computer system 1200. In particularembodiments, one or more computer systems 1200 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1200 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1200 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1200.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1200. This disclosure contemplates computer system 1200 taking anysuitable physical form. As example and not by way of limitation,computer system 1200 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1200 may include one or more computersystems 1200; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1200 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1200 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1200 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1200 includes a processor1202, memory 1204, storage 1206, an input/output (I/O) interface 1208, acommunication interface 1210, and a bus 1212. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1202 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1202 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1204, or storage 1206; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1204, or storage 1206. In particularembodiments, processor 1202 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1202 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1202 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1204 or storage 1206, and the instruction caches may speed upretrieval of those instructions by processor 1202. Data in the datacaches may be copies of data in memory 1204 or storage 1206 forinstructions executing at processor 1202 to operate on; the results ofprevious instructions executed at processor 1202 for access bysubsequent instructions executing at processor 1202 or for writing tomemory 1204 or storage 1206; or other suitable data. The data caches mayspeed up read or write operations by processor 1202. The TLBs may speedup virtual-address translation for processor 1202. In particularembodiments, processor 1202 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1202 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1202 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1202. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1204 includes main memory for storinginstructions for processor 1202 to execute or data for processor 1202 tooperate on. As an example and not by way of limitation, computer system1200 may load instructions from storage 1206 or another source (such as,for example, another computer system 1200) to memory 1204. Processor1202 may then load the instructions from memory 1204 to an internalregister or internal cache. To execute the instructions, processor 1202may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1202 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1202 may then write one or more of those results to memory 1204. Inparticular embodiments, processor 1202 executes only instructions in oneor more internal registers or internal caches or in memory 1204 (asopposed to storage 1206 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1204 (asopposed to storage 1206 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1202 to memory 1204. Bus 1212 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1202 and memory 1204and facilitate accesses to memory 1204 requested by processor 1202. Inparticular embodiments, memory 1204 includes random access memory (RAM).This RAM may be volatile memory, where appropriate Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1204 may include one ormore memories 1204, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1206 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1206 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1206 may include removable or non-removable (or fixed)media, where appropriate. Storage 1206 may be internal or external tocomputer system 1200, where appropriate. In particular embodiments,storage 1206 is non-volatile, solid-state memory. In particularembodiments, storage 1206 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1206taking any suitable physical form. Storage 1206 may include one or morestorage control units facilitating communication between processor 1202and storage 1206, where appropriate. Where appropriate, storage 1206 mayinclude one or more storages 1206. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1208 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1200 and one or more I/O devices. Computersystem 1200 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1200. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1208 for them. Where appropriate, I/Ointerface 1208 may include one or more device or software driversenabling processor 1202 to drive one or more of these I/O devices. I/Ointerface 1208 may include one or more I/O interfaces 1208, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1210 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1200 and one or more other computer systems 1200 or oneor more networks. As an example and not by way of limitation,communication interface 1210 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1210 for it. As an example and not by way oflimitation, computer system 1200 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1200 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1200 may include any suitable communicationinterface 1210 for any of these networks, where appropriate.Communication interface 1210 may include one or more communicationinterfaces 1210, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1212 includes hardware, software, or bothcoupling components of computer system 1200 to each other. As an exampleand not by way of limitation, bus 1212 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1212may include one or more buses 1212, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative.

What is claimed is:
 1. A method comprising, by one or more computingdevices: accessing, by the one or more computing devices, a plurality ofcommunications authored by one or more users of an online socialnetwork, each communication being associated with a particular contentitem and comprising a text of the communication; extracting, for each ofthe plurality of communications, one or more n-grams from the text ofthe communication; identifying, by the one or more computing devices,one or more mention-terms from the one or more extracted n-grams, eachmention-term being a noun-phrase; calculating, by the one or morecomputing devices, a term-score for each mention-term based on afrequency of occurrence of the mention-term in the plurality ofcommunications; and generating, by the one or more computing devices, amentions-module comprising one or more mentions, each mention comprisinga mention-term having a term-score greater than a threshold term-scoreand text from one or more communications comprising the mention-term. 2.The method of claim 1, further comprising: accessing a social graphcomprising a plurality of nodes and a plurality of edges connecting thenodes, each of the edges between two of the nodes representing a singledegree of separation between them, the nodes comprising: a first nodecorresponding to a first user associated with the online social network;and a plurality of second nodes corresponding to a plurality ofcommunications of the online social network.
 3. The method of claim 1,wherein the each communication is associated with the particular contentitem if the each communication includes a link to an instance of theparticular content item.
 4. The method of claim 1, wherein theidentification of the one or more mention-terms from the one or moreextracted n-grams comprises: determining one or more noun phrases fromthe extracted one or more n-grams based on a part-of-speech analysis;and filtering out one or more of the noun-phrases based on a level ofrelevance of the noun-phrases with respect to the particular contentitem as determined by a TF-IDF algorithm.
 5. The method of claim 4,wherein the determination of the level of relevance by the TF-IDFalgorithm is based on a frequency of usage of the one or more n-grams incommunications associated with other content items related to a topicshared in common with the particular content item.
 6. The method ofclaim 1, wherein the term-score of a mention-term is further based onone or more social signals associated with one or more of thecommunications comprising the mention-term.
 7. The method of claim 1,wherein the term-score of a mention-term is further based on demographicinformation associated with one or more authors of one or more of thecommunications comprising the mention-term.
 8. The method of claim 1,wherein the term-score of a mention-term is further based on whether themention-term is included in one or more other search-results modulesgenerated for the particular content item.
 9. The method of claim 1,further comprising sending, to the client system of a first user of theonline social network, the mentions-module for display to the firstuser.
 10. The method of claim 9, wherein the term-score of amention-term is further based on a location associated with the clientsystem of the first user.
 11. The method of claim 9, wherein the textfrom the one or more communications in each mention is extracted fromone or more representative communications from the plurality ofcommunications, each of the one or more representative communicationshaving a representative-communication-score greater than a thresholdrepresentative-communication-score, therepresentative-communication-score for the representative communicationbeing based on an affinity coefficient between the first user and anauthor of the representative communication.
 12. The method of claim 11,wherein the text extracted from a representative communication comprisesone or more n-grams preceding or following the one or more mention-termswithin the representative communication.
 13. The method of claim 11,wherein the representative-communication-score is further based ondemographic information associated with the first user.
 14. The methodof claim 11, wherein the representative-communication-score is furtherbased on a language associated with the first user.
 15. The method ofclaim 11, wherein the representative-communication-score is furtherbased on a user preference associated with the first user.
 16. Themethod of claim 11, wherein the representative-communication-score isfurther based on a respective text quality of the representativecommunication.
 17. The method of claim 1, wherein the mentions-module isgenerated for the particular content item if there are a thresholdnumber of communications associated with the particular content item.18. The method of claim 1, wherein the one or more mentions are purgedfrom the one or more computing devices after a pre-defined period oftime if its respective frequency of occurrence of the mention-termsincluded in the mentions is less than a minimum value within theplurality of communications.
 19. One or more computer-readablenon-transitory storage media embodying software that is operable whenexecuted to: access a plurality of communications authored by one ormore users of an online social network, each communication beingassociated with a particular content item and comprising a text of thecommunication; extract, for each of the plurality of communications, oneor more n-grams from the text of the communication; identify one or moremention-terms from the one or more extracted n-grams, each mention-termbeing a noun-phrase; calculate a term-score for each mention-term basedon a frequency of occurrence of the mention-term in the plurality ofcommunications; and generate a mentions-module comprising one or morementions, each mention comprising a mention-term having a term-scoregreater than a threshold term-score and text from one or morecommunications comprising the mention-term.
 20. A system comprising: oneor more processors; and a non-transitory memory coupled to theprocessors comprising instructions executable by the processors, theprocessors operable when executing the instructions to: access aplurality of communications authored by one or more users of an onlinesocial network, each communication being associated with a particularcontent item and comprising a text of the communication; extract, foreach of the plurality of communications, one or more n-grams from thetext of the communication; identify one or more mention-terms from theone or more extracted n-grams, each mention-term being a noun-phrase;calculate a term-score for each mention-term based on a frequency ofoccurrence of the mention-term in the plurality of communications; andgenerate a mentions-module comprising one or more mentions, each mentioncomprising a mention-term having a term-score greater than a thresholdterm-score and text from one or more communications comprising themention-term.